id	sid	tid	token	lemma	pos
esrj-50337	1	1	an	an	DET
esrj-50337	1	2	artificial	artificial	ADJ
esrj-50337	1	3	neural	neural	ADJ
esrj-50337	1	4	network	network	NOUN
esrj-50337	1	5	was	be	AUX
esrj-50337	1	6	used	use	VERB
esrj-50337	1	7	for	for	ADP
esrj-50337	1	8	forecasting	forecasting	NOUN
esrj-50337	1	9	of	of	ADP
esrj-50337	1	10	long	long	ADJ
esrj-50337	1	11	-	-	PUNCT
esrj-50337	1	12	term	term	NOUN
esrj-50337	1	13	wind	wind	NOUN
esrj-50337	1	14	speed	speed	NOUN
esrj-50337	1	15	data	datum	NOUN
esrj-50337	1	16	(	(	PUNCT
esrj-50337	1	17	24	24	NUM
esrj-50337	1	18	and	and	CCONJ
esrj-50337	1	19	48	48	NUM
esrj-50337	1	20	hours	hour	NOUN
esrj-50337	1	21	ahead	ahead	ADV
esrj-50337	1	22	)	)	PUNCT
esrj-50337	1	23	in	in	ADP
esrj-50337	1	24	la	la	PRON
esrj-50337	1	25	serena	serena	PROPN
esrj-50337	1	26	city	city	PROPN
esrj-50337	1	27	(	(	PUNCT
esrj-50337	1	28	chile	chile	PROPN
esrj-50337	1	29	)	)	PUNCT
esrj-50337	1	30	.	.	PUNCT
esrj-50337	2	1	in	in	ADP
esrj-50337	2	2	order	order	NOUN
esrj-50337	2	3	to	to	PART
esrj-50337	2	4	obtain	obtain	VERB
esrj-50337	2	5	a	a	DET
esrj-50337	2	6	more	more	ADV
esrj-50337	2	7	effective	effective	ADJ
esrj-50337	2	8	correlation	correlation	NOUN
esrj-50337	2	9	and	and	CCONJ
esrj-50337	2	10	prediction	prediction	NOUN
esrj-50337	2	11	,	,	PUNCT
esrj-50337	2	12	a	a	DET
esrj-50337	2	13	particle	particle	NOUN
esrj-50337	2	14	swarm	swarm	NOUN
esrj-50337	2	15	algorithm	algorithm	NOUN
esrj-50337	2	16	was	be	AUX
esrj-50337	2	17	implemented	implement	VERB
esrj-50337	2	18	to	to	PART
esrj-50337	2	19	update	update	VERB
esrj-50337	2	20	the	the	DET
esrj-50337	2	21	weights	weight	NOUN
esrj-50337	2	22	of	of	ADP
esrj-50337	2	23	the	the	DET
esrj-50337	2	24	network	network	NOUN
esrj-50337	2	25	.	.	PUNCT
esrj-50337	3	1	43800	43800	NUM
esrj-50337	3	2	data	datum	NOUN
esrj-50337	3	3	points	point	NOUN
esrj-50337	3	4	of	of	ADP
esrj-50337	3	5	wind	wind	NOUN
esrj-50337	3	6	speed	speed	NOUN
esrj-50337	3	7	were	be	AUX
esrj-50337	3	8	used	use	VERB
esrj-50337	3	9	(	(	PUNCT
esrj-50337	3	10	years	year	NOUN
esrj-50337	3	11	20032007	20032007	NUM
esrj-50337	3	12	)	)	PUNCT
esrj-50337	3	13	,	,	PUNCT
esrj-50337	3	14	and	and	CCONJ
esrj-50337	3	15	the	the	DET
esrj-50337	3	16	past	past	ADJ
esrj-50337	3	17	values	value	NOUN
esrj-50337	3	18	of	of	ADP
esrj-50337	3	19	wind	wind	NOUN
esrj-50337	3	20	speed	speed	NOUN
esrj-50337	3	21	,	,	PUNCT
esrj-50337	3	22	relative	relative	ADJ
esrj-50337	3	23	humidity	humidity	NOUN
esrj-50337	3	24	,	,	PUNCT
esrj-50337	3	25	and	and	CCONJ
esrj-50337	3	26	air	air	NOUN
esrj-50337	3	27	temperature	temperature	NOUN
esrj-50337	3	28	were	be	AUX
esrj-50337	3	29	used	use	VERB
esrj-50337	3	30	as	as	ADP
esrj-50337	3	31	input	input	NOUN
esrj-50337	3	32	parameters	parameter	NOUN
esrj-50337	3	33	,	,	PUNCT
esrj-50337	3	34	considering	consider	VERB
esrj-50337	3	35	that	that	SCONJ
esrj-50337	3	36	these	these	DET
esrj-50337	3	37	meteorogical	meteorogical	ADJ
esrj-50337	3	38	parameters	parameter	NOUN
esrj-50337	3	39	are	be	AUX
esrj-50337	3	40	more	more	ADV
esrj-50337	3	41	readily	readily	ADV
esrj-50337	3	42	available	available	ADJ
esrj-50337	3	43	around	around	ADP
esrj-50337	3	44	the	the	DET
esrj-50337	3	45	globe	globe	NOUN
esrj-50337	3	46	.	.	PUNCT
esrj-50337	4	1	several	several	ADJ
esrj-50337	4	2	neural	neural	ADJ
esrj-50337	4	3	network	network	NOUN
esrj-50337	4	4	architectures	architecture	NOUN
esrj-50337	4	5	were	be	AUX
esrj-50337	4	6	studied	study	VERB
esrj-50337	4	7	,	,	PUNCT
esrj-50337	4	8	and	and	CCONJ
esrj-50337	4	9	the	the	DET
esrj-50337	4	10	optimum	optimum	ADJ
esrj-50337	4	11	architecture	architecture	NOUN
esrj-50337	4	12	was	be	AUX
esrj-50337	4	13	determined	determine	VERB
esrj-50337	4	14	by	by	ADP
esrj-50337	4	15	adding	add	VERB
esrj-50337	4	16	neurons	neuron	NOUN
esrj-50337	4	17	in	in	ADP
esrj-50337	4	18	systematic	systematic	ADJ
esrj-50337	4	19	form	form	NOUN
esrj-50337	4	20	and	and	CCONJ
esrj-50337	4	21	evaluating	evaluate	VERB
esrj-50337	4	22	the	the	DET
esrj-50337	4	23	root	root	NOUN
esrj-50337	4	24	mean	mean	ADJ
esrj-50337	4	25	square	square	ADJ
esrj-50337	4	26	error	error	NOUN
esrj-50337	4	27	(	(	PUNCT
esrj-50337	4	28	rmse	rmse	NOUN
esrj-50337	4	29	)	)	PUNCT
esrj-50337	4	30	during	during	ADP
esrj-50337	4	31	the	the	DET
esrj-50337	4	32	learning	learning	NOUN
esrj-50337	4	33	process	process	NOUN
esrj-50337	4	34	.	.	PUNCT
esrj-50337	5	1	the	the	DET
esrj-50337	5	2	results	result	NOUN
esrj-50337	5	3	show	show	VERB
esrj-50337	5	4	that	that	SCONJ
esrj-50337	5	5	the	the	DET
esrj-50337	5	6	meteorological	meteorological	ADJ
esrj-50337	5	7	variables	variable	NOUN
esrj-50337	5	8	used	use	VERB
esrj-50337	5	9	as	as	ADP
esrj-50337	5	10	input	input	NOUN
esrj-50337	5	11	parameters	parameter	NOUN
esrj-50337	5	12	,	,	PUNCT
esrj-50337	5	13	have	have	VERB
esrj-50337	5	14	influential	influential	ADJ
esrj-50337	5	15	effects	effect	NOUN
esrj-50337	5	16	on	on	ADP
esrj-50337	5	17	the	the	DET
esrj-50337	5	18	good	good	ADJ
esrj-50337	5	19	training	training	NOUN
esrj-50337	5	20	and	and	CCONJ
esrj-50337	5	21	predicting	predict	VERB
esrj-50337	5	22	capabilities	capability	NOUN
esrj-50337	5	23	of	of	ADP
esrj-50337	5	24	the	the	DET
esrj-50337	5	25	chosen	choose	VERB
esrj-50337	5	26	network	network	NOUN
esrj-50337	5	27	,	,	PUNCT
esrj-50337	5	28	and	and	CCONJ
esrj-50337	5	29	that	that	SCONJ
esrj-50337	5	30	the	the	DET
esrj-50337	5	31	hybrid	hybrid	ADJ
esrj-50337	5	32	neural	neural	ADJ
esrj-50337	5	33	network	network	NOUN
esrj-50337	5	34	can	can	AUX
esrj-50337	5	35	forecast	forecast	VERB
esrj-50337	5	36	the	the	DET
esrj-50337	5	37	hourly	hourly	ADJ
esrj-50337	5	38	wind	wind	NOUN
esrj-50337	5	39	speed	speed	NOUN
esrj-50337	5	40	with	with	ADP
esrj-50337	5	41	acceptable	acceptable	ADJ
esrj-50337	5	42	accuracy	accuracy	NOUN
esrj-50337	5	43	,	,	PUNCT
esrj-50337	5	44	such	such	ADJ
esrj-50337	5	45	as	as	ADP
esrj-50337	5	46	:	:	PUNCT
esrj-50337	5	47	rmse=0.81	rmse=0.81	PROPN
esrj-50337	5	48	[	[	X
esrj-50337	5	49	m·s−1	m·s−1	NOUN
esrj-50337	5	50	]	]	PUNCT
esrj-50337	5	51	,	,	PUNCT
esrj-50337	5	52	mse=0.65	mse=0.65	PROPN
esrj-50337	6	1	[	[	X
esrj-50337	6	2	m·s−1]2	m·s−1]2	NOUN
esrj-50337	6	3	and	and	CCONJ
esrj-50337	6	4	r2=0.97	r2=0.97	NOUN
esrj-50337	6	5	for	for	ADP
esrj-50337	6	6	24	24	NUM
esrj-50337	6	7	-	-	PUNCT
esrj-50337	6	8	hours	hour	NOUN
esrj-50337	6	9	-	-	PUNCT
esrj-50337	6	10	ahead	ahead	NOUN
esrj-50337	6	11	wind	wind	NOUN
esrj-50337	6	12	speed	speed	NOUN
esrj-50337	6	13	prediction	prediction	NOUN
esrj-50337	6	14	,	,	PUNCT
esrj-50337	6	15	and	and	CCONJ
esrj-50337	6	16	rmse=0.78	rmse=0.78	PROPN
esrj-50337	6	17	,	,	PUNCT
esrj-50337	6	18	mse=0.634	mse=0.634	PROPN
esrj-50337	6	19	[	[	X
esrj-50337	6	20	m·s−1]2	m·s−1]2	NOUN
esrj-50337	6	21	and	and	CCONJ
esrj-50337	6	22	r2=0.97	r2=0.97	NOUN
esrj-50337	6	23	for	for	ADP
esrj-50337	6	24	48	48	NUM
esrj-50337	6	25	-	-	PUNCT
esrj-50337	6	26	hours	hour	NOUN
esrj-50337	6	27	-	-	PUNCT
esrj-50337	6	28	ahead	ahead	NOUN
esrj-50337	6	29	wind	wind	NOUN
esrj-50337	6	30	speed	speed	NOUN
esrj-50337	6	31	prediction	prediction	NOUN
esrj-50337	6	32	.	.	PUNCT
esrj-50337	7	1	abstract	abstract	ADJ
esrj-50337	7	2	keywords	keyword	NOUN
esrj-50337	7	3	:	:	PUNCT
esrj-50337	7	4	wind	wind	NOUN
esrj-50337	7	5	speed	speed	NOUN
esrj-50337	7	6	;	;	PUNCT
esrj-50337	7	7	time	time	NOUN
esrj-50337	7	8	series	series	PROPN
esrj-50337	7	9	forecasting	forecasting	PROPN
esrj-50337	7	10	;	;	PUNCT
esrj-50337	7	11	artificial	artificial	ADJ
esrj-50337	7	12	neural	neural	ADJ
esrj-50337	7	13	network	network	NOUN
esrj-50337	7	14	;	;	PUNCT
esrj-50337	7	15	particle	particle	NOUN
esrj-50337	7	16	swarm	swarm	NOUN
esrj-50337	7	17	optimization	optimization	NOUN
esrj-50337	7	18	;	;	PUNCT
esrj-50337	7	19	meteorological	meteorological	ADJ
esrj-50337	7	20	data	datum	NOUN
esrj-50337	7	21	.	.	PUNCT
esrj-50337	8	1	long	long	ADJ
esrj-50337	8	2	-	-	PUNCT
esrj-50337	8	3	term	term	NOUN
esrj-50337	8	4	prediction	prediction	NOUN
esrj-50337	8	5	of	of	ADP
esrj-50337	8	6	wind	wind	NOUN
esrj-50337	8	7	speed	speed	NOUN
esrj-50337	8	8	in	in	ADP
esrj-50337	8	9	la	la	PRON
esrj-50337	8	10	serena	serena	PROPN
esrj-50337	8	11	city	city	PROPN
esrj-50337	8	12	(	(	PUNCT
esrj-50337	8	13	chile	chile	PROPN
esrj-50337	8	14	)	)	PUNCT
esrj-50337	8	15	using	use	VERB
esrj-50337	8	16	hybrid	hybrid	ADJ
esrj-50337	8	17	neural	neural	ADJ
esrj-50337	8	18	network	network	NOUN
esrj-50337	8	19	-	-	PUNCT
esrj-50337	8	20	particle	particle	NOUN
esrj-50337	8	21	swarm	swarm	NOUN
esrj-50337	8	22	algorithm	algorithm	NOUN
esrj-50337	8	23	predicción	predicción	PROPN
esrj-50337	8	24	a	a	DET
esrj-50337	8	25	largo	largo	PROPN
esrj-50337	8	26	plazo	plazo	X
esrj-50337	8	27	de	de	X
esrj-50337	8	28	la	la	X
esrj-50337	8	29	velocidad	velocidad	PROPN
esrj-50337	8	30	de	de	PROPN
esrj-50337	8	31	viento	viento	PROPN
esrj-50337	8	32	en	en	PROPN
esrj-50337	8	33	la	la	PROPN
esrj-50337	8	34	ciudad	ciudad	PROPN
esrj-50337	8	35	de	de	PROPN
esrj-50337	8	36	la	la	X
esrj-50337	8	37	serena	serena	PROPN
esrj-50337	8	38	(	(	PUNCT
esrj-50337	8	39	chile	chile	PROPN
esrj-50337	8	40	)	)	PUNCT
esrj-50337	8	41	utilizando	utilizando	PROPN
esrj-50337	8	42	un	un	PROPN
esrj-50337	8	43	algoritmo	algoritmo	PROPN
esrj-50337	8	44	híbrido	híbrido	PROPN
esrj-50337	8	45	de	de	X
esrj-50337	8	46	red	red	ADJ
esrj-50337	8	47	neuronal	neuronal	ADJ
esrj-50337	8	48	-	-	PUNCT
esrj-50337	8	49	enjambre	enjambre	NOUN
esrj-50337	8	50	de	de	PROPN
esrj-50337	8	51	partículas	partículas	PROPN
esrj-50337	8	52	issn	issn	PROPN
esrj-50337	8	53	1794	1794	NUM
esrj-50337	8	54	-	-	SYM
esrj-50337	8	55	6190	6190	NUM
esrj-50337	8	56	e	e	NOUN
esrj-50337	8	57	-	-	NOUN
esrj-50337	8	58	issn	issn	PROPN
esrj-50337	8	59	2339	2339	NUM
esrj-50337	8	60	-	-	SYM
esrj-50337	8	61	3459	3459	NUM
esrj-50337	8	62	http://dx.doi.org/10.15446/esrj.v21n1.50337	http://dx.doi.org/10.15446/esrj.v21n1.50337	PROPN
esrj-50337	8	63	juan	juan	PROPN
esrj-50337	8	64	a.	a.	PROPN
esrj-50337	8	65	lazzús	lazzús	PROPN
esrj-50337	8	66	*	*	PUNCT
esrj-50337	8	67	and	and	CCONJ
esrj-50337	8	68	ignacio	ignacio	PROPN
esrj-50337	8	69	salfate	salfate	PROPN
esrj-50337	8	70	departamento	departamento	PROPN
esrj-50337	8	71	de	de	PROPN
esrj-50337	8	72	física	física	PROPN
esrj-50337	8	73	y	y	PROPN
esrj-50337	8	74	astronomía	astronomía	PROPN
esrj-50337	8	75	,	,	PUNCT
esrj-50337	8	76	universidad	universidad	PROPN
esrj-50337	8	77	de	de	X
esrj-50337	8	78	la	la	X
esrj-50337	8	79	serena	serena	PROPN
esrj-50337	8	80	,	,	PUNCT
esrj-50337	8	81	casilla	casilla	X
esrj-50337	8	82	554	554	NUM
esrj-50337	8	83	,	,	PUNCT
esrj-50337	8	84	la	la	ADJ
esrj-50337	8	85	serena	serena	NOUN
esrj-50337	8	86	,	,	PUNCT
esrj-50337	8	87	chile	chile	PROPN
esrj-50337	8	88	*	*	PROPN
esrj-50337	8	89	jlazzus@dfuls.cl	jlazzus@dfuls.cl	PROPN
esrj-50337	8	90	una	una	PROPN
esrj-50337	8	91	red	red	PROPN
esrj-50337	8	92	neuronal	neuronal	ADJ
esrj-50337	8	93	artificial	artificial	ADJ
esrj-50337	8	94	fue	fue	PROPN
esrj-50337	8	95	utilizada	utilizada	PROPN
esrj-50337	8	96	para	para	PROPN
esrj-50337	8	97	la	la	PROPN
esrj-50337	8	98	predicción	predicción	PROPN
esrj-50337	8	99	de	de	X
esrj-50337	8	100	datos	datos	X
esrj-50337	8	101	de	de	X
esrj-50337	8	102	la	la	X
esrj-50337	8	103	velocidad	velocidad	PROPN
esrj-50337	8	104	de	de	PROPN
esrj-50337	8	105	viento	viento	PROPN
esrj-50337	8	106	a	a	DET
esrj-50337	8	107	largo	largo	PROPN
esrj-50337	8	108	plazo	plazo	NOUN
esrj-50337	8	109	(	(	PUNCT
esrj-50337	8	110	24	24	NUM
esrj-50337	8	111	y	y	PROPN
esrj-50337	8	112	48	48	NUM
esrj-50337	8	113	horas	hora	NOUN
esrj-50337	8	114	en	en	X
esrj-50337	8	115	adelanto	adelanto	PROPN
esrj-50337	8	116	)	)	PUNCT
esrj-50337	8	117	en	en	X
esrj-50337	8	118	la	la	PROPN
esrj-50337	8	119	ciudad	ciudad	PROPN
esrj-50337	8	120	de	de	PROPN
esrj-50337	8	121	la	la	X
esrj-50337	8	122	serena	serena	PROPN
esrj-50337	8	123	(	(	PUNCT
esrj-50337	8	124	chile	chile	PROPN
esrj-50337	8	125	)	)	PUNCT
esrj-50337	8	126	.	.	PUNCT
esrj-50337	9	1	para	para	PROPN
esrj-50337	9	2	obtener	obtener	PROPN
esrj-50337	9	3	una	una	PROPN
esrj-50337	9	4	efectiva	efectiva	PROPN
esrj-50337	9	5	correlación	correlación	PROPN
esrj-50337	9	6	y	y	PROPN
esrj-50337	9	7	predición	predición	PROPN
esrj-50337	9	8	,	,	PUNCT
esrj-50337	9	9	se	se	PROPN
esrj-50337	9	10	implementó	implementó	PROPN
esrj-50337	9	11	una	una	PROPN
esrj-50337	9	12	optimización	optimización	PROPN
esrj-50337	9	13	de	de	X
esrj-50337	9	14	enjambre	enjambre	PROPN
esrj-50337	9	15	de	de	PROPN
esrj-50337	9	16	particulas	particulas	PROPN
esrj-50337	9	17	para	para	PROPN
esrj-50337	9	18	actualizar	actualizar	PROPN
esrj-50337	9	19	los	los	PROPN
esrj-50337	9	20	pesos	pesos	PROPN
esrj-50337	9	21	de	de	X
esrj-50337	9	22	la	la	X
esrj-50337	9	23	red	red	PROPN
esrj-50337	9	24	.	.	PUNCT
esrj-50337	9	25	se	se	PROPN
esrj-50337	9	26	emplearon	emplearon	PROPN
esrj-50337	9	27	43800	43800	NUM
esrj-50337	9	28	datos	datos	X
esrj-50337	9	29	de	de	X
esrj-50337	9	30	velocidad	velocidad	PROPN
esrj-50337	9	31	de	de	PROPN
esrj-50337	9	32	viento	viento	PROPN
esrj-50337	9	33	(	(	PUNCT
esrj-50337	9	34	años	años	PROPN
esrj-50337	9	35	2003	2003	NUM
esrj-50337	9	36	-	-	SYM
esrj-50337	9	37	2007	2007	NUM
esrj-50337	9	38	)	)	PUNCT
esrj-50337	9	39	,	,	PUNCT
esrj-50337	9	40	y	y	PROPN
esrj-50337	9	41	los	los	PROPN
esrj-50337	9	42	valores	valores	PROPN
esrj-50337	9	43	pasados	pasados	PROPN
esrj-50337	9	44	de	de	PROPN
esrj-50337	9	45	velocidad	velocidad	PROPN
esrj-50337	9	46	del	del	PROPN
esrj-50337	9	47	viento	viento	PROPN
esrj-50337	9	48	,	,	PUNCT
esrj-50337	9	49	humedad	humedad	PROPN
esrj-50337	9	50	relativa	relativa	PROPN
esrj-50337	9	51	y	y	PROPN
esrj-50337	9	52	temperatura	temperatura	PROPN
esrj-50337	9	53	del	del	PROPN
esrj-50337	9	54	aire	aire	PROPN
esrj-50337	9	55	fueron	fueron	PROPN
esrj-50337	9	56	utilizados	utilizado	NOUN
esrj-50337	9	57	como	como	PROPN
esrj-50337	9	58	parámetros	parámetros	PROPN
esrj-50337	9	59	de	de	PROPN
esrj-50337	9	60	entrada	entrada	PROPN
esrj-50337	9	61	,	,	PUNCT
esrj-50337	9	62	considerando	considerando	PROPN
esrj-50337	9	63	que	que	PROPN
esrj-50337	9	64	estos	estos	PROPN
esrj-50337	9	65	parámetros	parámetros	PROPN
esrj-50337	9	66	meteorológicos	meteorológicos	PROPN
esrj-50337	9	67	se	se	PROPN
esrj-50337	9	68	encuentran	encuentran	PROPN
esrj-50337	9	69	fácilmente	fácilmente	PROPN
esrj-50337	9	70	disponibles	disponibles	PROPN
esrj-50337	9	71	en	en	X
esrj-50337	9	72	todo	todo	PROPN
esrj-50337	9	73	el	el	PROPN
esrj-50337	9	74	mundo	mundo	PROPN
esrj-50337	9	75	.	.	PUNCT
esrj-50337	10	1	se	se	PROPN
esrj-50337	10	2	estudiaron	estudiaron	PROPN
esrj-50337	10	3	varias	varias	PROPN
esrj-50337	10	4	arquitecturas	arquitecturas	PROPN
esrj-50337	10	5	de	de	PROPN
esrj-50337	10	6	redes	redes	PROPN
esrj-50337	10	7	neuronales	neuronale	VERB
esrj-50337	10	8	y	y	PROPN
esrj-50337	10	9	la	la	PROPN
esrj-50337	10	10	arquitectura	arquitectura	PROPN
esrj-50337	10	11	optima	optima	PROPN
esrj-50337	10	12	se	se	PROPN
esrj-50337	10	13	determine	determine	VERB
esrj-50337	10	14	añadiendo	añadiendo	PROPN
esrj-50337	10	15	neuronas	neuronas	PROPN
esrj-50337	10	16	de	de	PROPN
esrj-50337	10	17	forma	forma	PROPN
esrj-50337	10	18	sistemática	sistemática	PROPN
esrj-50337	10	19	y	y	PROPN
esrj-50337	10	20	evaluando	evaluando	PROPN
esrj-50337	10	21	la	la	PROPN
esrj-50337	10	22	raíz	raíz	PROPN
esrj-50337	10	23	del	del	PROPN
esrj-50337	10	24	error	error	NOUN
esrj-50337	10	25	cuadrático	cuadrático	NOUN
esrj-50337	10	26	medio	medio	NOUN
esrj-50337	10	27	(	(	PUNCT
esrj-50337	10	28	rmse	rmse	PROPN
esrj-50337	10	29	)	)	PUNCT
esrj-50337	10	30	durante	durante	PROPN
esrj-50337	10	31	el	el	PROPN
esrj-50337	10	32	proceso	proceso	PROPN
esrj-50337	10	33	de	de	PROPN
esrj-50337	10	34	aprendizaje	aprendizaje	PROPN
esrj-50337	10	35	.	.	PUNCT
esrj-50337	11	1	los	los	PROPN
esrj-50337	11	2	resultados	resultado	VERB
esrj-50337	11	3	muestran	muestran	ADJ
esrj-50337	11	4	que	que	PROPN
esrj-50337	11	5	las	las	PROPN
esrj-50337	11	6	variables	variables	PROPN
esrj-50337	11	7	meteorológicas	meteorológica	VERB
esrj-50337	11	8	utilizadas	utilizadas	PROPN
esrj-50337	11	9	como	como	PROPN
esrj-50337	11	10	parámetros	parámetros	PROPN
esrj-50337	11	11	de	de	PROPN
esrj-50337	11	12	entrada	entrada	PROPN
esrj-50337	11	13	,	,	PUNCT
esrj-50337	11	14	tienen	tienen	PROPN
esrj-50337	11	15	un	un	PROPN
esrj-50337	11	16	efecto	efecto	PROPN
esrj-50337	11	17	positivo	positivo	PROPN
esrj-50337	11	18	sobre	sobre	PROPN
esrj-50337	11	19	el	el	PROPN
esrj-50337	11	20	correcto	correcto	PROPN
esrj-50337	11	21	entrenamiento	entrenamiento	PROPN
esrj-50337	11	22	y	y	PROPN
esrj-50337	11	23	capacidades	capacidade	VERB
esrj-50337	11	24	predictivas	predictivas	PROPN
esrj-50337	11	25	de	de	X
esrj-50337	11	26	la	la	PROPN
esrj-50337	11	27	red	red	PROPN
esrj-50337	11	28	,	,	PUNCT
esrj-50337	11	29	y	y	PROPN
esrj-50337	11	30	que	que	PROPN
esrj-50337	11	31	la	la	PROPN
esrj-50337	11	32	red	red	PROPN
esrj-50337	11	33	neural	neural	PROPN
esrj-50337	11	34	híbrida	híbrida	PROPN
esrj-50337	11	35	puede	puede	PROPN
esrj-50337	11	36	pronosticar	pronosticar	PROPN
esrj-50337	11	37	la	la	PROPN
esrj-50337	11	38	velocidad	velocidad	PROPN
esrj-50337	11	39	del	del	PROPN
esrj-50337	11	40	viento	viento	PROPN
esrj-50337	11	41	horaria	horaria	PROPN
esrj-50337	11	42	con	con	PROPN
esrj-50337	11	43	una	una	PROPN
esrj-50337	11	44	precisión	precisión	PROPN
esrj-50337	11	45	aceptable	aceptable	PROPN
esrj-50337	11	46	,	,	PUNCT
esrj-50337	11	47	como	como	PROPN
esrj-50337	11	48	un	un	PROPN
esrj-50337	11	49	rmse=0.81	rmse=0.81	PROPN
esrj-50337	12	1	[	[	X
esrj-50337	12	2	m·s−1	m·s−1	NOUN
esrj-50337	12	3	]	]	PUNCT
esrj-50337	12	4	,	,	PUNCT
esrj-50337	12	5	mse=0.65	mse=0.65	PROPN
esrj-50337	13	1	[	[	X
esrj-50337	13	2	m·s−1]2	m·s−1]2	PROPN
esrj-50337	13	3	y	y	PROPN
esrj-50337	13	4	r2=0.97	r2=0.97	NOUN
esrj-50337	13	5	para	para	VERB
esrj-50337	13	6	la	la	INTJ
esrj-50337	13	7	predicción	predicción	PROPN
esrj-50337	13	8	de	de	PROPN
esrj-50337	13	9	la	la	X
esrj-50337	13	10	velocidad	velocidad	PROPN
esrj-50337	13	11	del	del	PROPN
esrj-50337	13	12	viento	viento	PROPN
esrj-50337	13	13	de	de	PROPN
esrj-50337	13	14	24	24	NUM
esrj-50337	13	15	horas	hora	NOUN
esrj-50337	13	16	en	en	ADP
esrj-50337	13	17	adelanto	adelanto	PROPN
esrj-50337	13	18	,	,	PUNCT
esrj-50337	13	19	y	y	PROPN
esrj-50337	13	20	un	un	PROPN
esrj-50337	13	21	rmse=0.78	rmse=0.78	PROPN
esrj-50337	13	22	,	,	PUNCT
esrj-50337	13	23	mse=0.634	mse=0.634	PROPN
esrj-50337	13	24	[	[	X
esrj-50337	13	25	m·s−1]2	m·s−1]2	ADJ
esrj-50337	13	26	and	and	CCONJ
esrj-50337	13	27	r2=0.97	r2=0.97	NOUN
esrj-50337	13	28	para	para	NOUN
esrj-50337	13	29	la	la	INTJ
esrj-50337	13	30	predicción	predicción	PROPN
esrj-50337	13	31	de	de	PROPN
esrj-50337	13	32	la	la	X
esrj-50337	13	33	velocidad	velocidad	PROPN
esrj-50337	13	34	del	del	PROPN
esrj-50337	13	35	viento	viento	PROPN
esrj-50337	13	36	de	de	PROPN
esrj-50337	13	37	48	48	NUM
esrj-50337	13	38	horas	hora	NOUN
esrj-50337	13	39	en	en	ADP
esrj-50337	13	40	adelanto	adelanto	PROPN
esrj-50337	13	41	.	.	PUNCT
esrj-50337	14	1	resumen	resumen	PROPN
esrj-50337	14	2	palabras	palabras	PROPN
esrj-50337	14	3	clave	clave	PROPN
esrj-50337	14	4	:	:	PUNCT
esrj-50337	14	5	velocidad	velocidad	PROPN
esrj-50337	14	6	del	del	PROPN
esrj-50337	14	7	viento	viento	PROPN
esrj-50337	14	8	;	;	PUNCT
esrj-50337	14	9	predicción	predicción	PROPN
esrj-50337	14	10	de	de	PROPN
esrj-50337	14	11	series	series	PROPN
esrj-50337	14	12	de	de	PROPN
esrj-50337	14	13	tiempo	tiempo	PROPN
esrj-50337	14	14	;	;	PUNCT
esrj-50337	14	15	redes	rede	NOUN
esrj-50337	14	16	neuronales	neuronale	NOUN
esrj-50337	14	17	artificiales	artificiale	NOUN
esrj-50337	14	18	;	;	PUNCT
esrj-50337	14	19	optimización	optimización	NOUN
esrj-50337	14	20	de	de	X
esrj-50337	14	21	enjambre	enjambre	PROPN
esrj-50337	14	22	de	de	PROPN
esrj-50337	14	23	particulas	particula	NOUN
esrj-50337	14	24	;	;	PUNCT
esrj-50337	14	25	datos	datos	PROPN
esrj-50337	14	26	meteorológicos	meteorológicos	PROPN
esrj-50337	14	27	.	.	PUNCT
esrj-50337	15	1	record	record	PROPN
esrj-50337	15	2	manuscript	manuscript	NOUN
esrj-50337	15	3	received	receive	VERB
esrj-50337	15	4	:	:	PUNCT
esrj-50337	15	5	30/04/2015	30/04/2015	NUM
esrj-50337	15	6	accepted	accept	VERB
esrj-50337	15	7	for	for	ADP
esrj-50337	15	8	publication	publication	NOUN
esrj-50337	15	9	:	:	PUNCT
esrj-50337	15	10	17/02/2017	17/02/2017	NUM
esrj-50337	15	11	how	how	SCONJ
esrj-50337	15	12	to	to	PART
esrj-50337	15	13	cite	cite	VERB
esrj-50337	15	14	item	item	NOUN
esrj-50337	15	15	lazzus	lazzus	NOUN
esrj-50337	15	16	,	,	PUNCT
esrj-50337	15	17	j.	j.	PROPN
esrj-50337	15	18	a.	a.	PROPN
esrj-50337	15	19	&	&	CCONJ
esrj-50337	15	20	salfate	salfate	PROPN
esrj-50337	15	21	,	,	PUNCT
esrj-50337	15	22	i.	i.	PROPN
esrj-50337	15	23	(	(	PUNCT
esrj-50337	15	24	2017	2017	NUM
esrj-50337	15	25	)	)	PUNCT
esrj-50337	15	26	.	.	PUNCT
esrj-50337	16	1	long	long	ADJ
esrj-50337	16	2	-	-	PUNCT
esrj-50337	16	3	term	term	NOUN
esrj-50337	16	4	prediction	prediction	NOUN
esrj-50337	16	5	of	of	ADP
esrj-50337	16	6	wind	wind	NOUN
esrj-50337	16	7	speed	speed	NOUN
esrj-50337	16	8	in	in	ADP
esrj-50337	16	9	la	la	PRON
esrj-50337	16	10	serena	serena	PROPN
esrj-50337	16	11	city	city	PROPN
esrj-50337	16	12	(	(	PUNCT
esrj-50337	16	13	chile	chile	PROPN
esrj-50337	16	14	)	)	PUNCT
esrj-50337	16	15	using	use	VERB
esrj-50337	16	16	hybrid	hybrid	ADJ
esrj-50337	16	17	neural	neural	ADJ
esrj-50337	16	18	network	network	NOUN
esrj-50337	16	19	-	-	PUNCT
esrj-50337	16	20	particle	particle	NOUN
esrj-50337	16	21	swarm	swarm	NOUN
esrj-50337	16	22	algorithm	algorithm	NOUN
esrj-50337	16	23	.	.	PUNCT
esrj-50337	17	1	earth	earth	PROPN
esrj-50337	17	2	sciences	sciences	PROPN
esrj-50337	17	3	research	research	PROPN
esrj-50337	17	4	journal	journal	NOUN
esrj-50337	17	5	,	,	PUNCT
esrj-50337	17	6	21(1	21(1	NUM
esrj-50337	17	7	)	)	PUNCT
esrj-50337	17	8	.	.	PUNCT
esrj-50337	18	1	29	29	NUM
esrj-50337	18	2	-	-	SYM
esrj-50337	18	3	35	35	NUM
esrj-50337	18	4	.	.	PUNCT
esrj-50337	19	1	doi	doi	NOUN
esrj-50337	19	2	:	:	PUNCT
esrj-50337	19	3	http://dx.doi.org/10.15446	http://dx.doi.org/10.15446	NOUN
esrj-50337	19	4	/	/	SYM
esrj-50337	19	5	esrj.v21n1.50337	esrj.v21n1.50337	NOUN
esrj-50337	19	6	m	m	VERB
esrj-50337	19	7	e	e	NOUN
esrj-50337	19	8	t	t	NOUN
esrj-50337	19	9	e	e	X
esrj-50337	19	10	o	o	NOUN
esrj-50337	19	11	r	r	NOUN
esrj-50337	19	12	o	o	NOUN
esrj-50337	19	13	l	l	NOUN
esrj-50337	19	14	o	o	X
esrj-50337	19	15	g	g	PROPN
esrj-50337	19	16	y	y	PROPN
esrj-50337	19	17	earth	earth	PROPN
esrj-50337	19	18	sciences	sciences	PROPN
esrj-50337	19	19	research	research	PROPN
esrj-50337	19	20	journal	journal	PROPN
esrj-50337	19	21	earth	earth	PROPN
esrj-50337	19	22	sci	sci	PROPN
esrj-50337	19	23	.	.	PUNCT
esrj-50337	20	1	res	res	PROPN
esrj-50337	20	2	.	.	PUNCT
esrj-50337	21	1	j.	j.	PROPN
esrj-50337	21	2	vol	vol	PROPN
esrj-50337	21	3	.	.	PROPN
esrj-50337	22	1	21	21	NUM
esrj-50337	22	2	,	,	PUNCT
esrj-50337	22	3	no	no	INTJ
esrj-50337	22	4	.	.	NOUN
esrj-50337	22	5	1	1	NUM
esrj-50337	22	6	(	(	PUNCT
esrj-50337	22	7	march	march	PROPN
esrj-50337	22	8	,	,	PUNCT
esrj-50337	22	9	2017	2017	NUM
esrj-50337	22	10	):	):	PUNCT
esrj-50337	22	11	29	29	NUM
esrj-50337	22	12	35	35	NUM
esrj-50337	22	13	30	30	NUM
esrj-50337	22	14	juan	juan	PROPN
esrj-50337	22	15	a.	a.	PROPN
esrj-50337	22	16	lazzús	lazzús	PROPN
esrj-50337	22	17	and	and	CCONJ
esrj-50337	22	18	ignacio	ignacio	PROPN
esrj-50337	22	19	salfate	salfate	VERB
esrj-50337	22	20	1	1	NUM
esrj-50337	22	21	.	.	PUNCT
esrj-50337	23	1	introduction	introduction	NOUN
esrj-50337	23	2	energy	energy	NOUN
esrj-50337	23	3	derived	derive	VERB
esrj-50337	23	4	from	from	ADP
esrj-50337	23	5	wind	wind	NOUN
esrj-50337	23	6	has	have	AUX
esrj-50337	23	7	played	play	VERB
esrj-50337	23	8	a	a	DET
esrj-50337	23	9	vital	vital	ADJ
esrj-50337	23	10	role	role	NOUN
esrj-50337	23	11	in	in	ADP
esrj-50337	23	12	the	the	DET
esrj-50337	23	13	history	history	NOUN
esrj-50337	23	14	of	of	ADP
esrj-50337	23	15	mankind	mankind	NOUN
esrj-50337	23	16	and	and	CCONJ
esrj-50337	23	17	is	be	AUX
esrj-50337	23	18	again	again	ADV
esrj-50337	23	19	receiving	receive	VERB
esrj-50337	23	20	considerable	considerable	ADJ
esrj-50337	23	21	attention	attention	NOUN
esrj-50337	23	22	because	because	SCONJ
esrj-50337	23	23	of	of	ADP
esrj-50337	23	24	its	its	PRON
esrj-50337	23	25	free	free	ADJ
esrj-50337	23	26	and	and	CCONJ
esrj-50337	23	27	non	non	ADJ
esrj-50337	23	28	-	-	ADJ
esrj-50337	23	29	polluting	polluting	ADJ
esrj-50337	23	30	character	character	NOUN
esrj-50337	23	31	.	.	PUNCT
esrj-50337	24	1	with	with	ADP
esrj-50337	24	2	the	the	DET
esrj-50337	24	3	development	development	NOUN
esrj-50337	24	4	of	of	ADP
esrj-50337	24	5	wind	wind	NOUN
esrj-50337	24	6	energy	energy	NOUN
esrj-50337	24	7	technologies	technology	NOUN
esrj-50337	24	8	and	and	CCONJ
esrj-50337	24	9	the	the	DET
esrj-50337	24	10	decrease	decrease	NOUN
esrj-50337	24	11	of	of	ADP
esrj-50337	24	12	wind	wind	NOUN
esrj-50337	24	13	power	power	NOUN
esrj-50337	24	14	production	production	NOUN
esrj-50337	24	15	cost	cost	NOUN
esrj-50337	24	16	,	,	PUNCT
esrj-50337	24	17	wind	wind	NOUN
esrj-50337	24	18	power	power	NOUN
esrj-50337	24	19	has	have	AUX
esrj-50337	24	20	rapidly	rapidly	ADV
esrj-50337	24	21	developed	develop	VERB
esrj-50337	24	22	around	around	ADP
esrj-50337	24	23	the	the	DET
esrj-50337	24	24	world	world	NOUN
esrj-50337	24	25	in	in	ADP
esrj-50337	24	26	recent	recent	ADJ
esrj-50337	24	27	years	year	NOUN
esrj-50337	24	28	(	(	PUNCT
esrj-50337	24	29	akdağ	akdağ	NOUN
esrj-50337	24	30	and	and	CCONJ
esrj-50337	24	31	guler	guler	NOUN
esrj-50337	24	32	,	,	PUNCT
esrj-50337	24	33	2011	2011	NUM
esrj-50337	24	34	)	)	PUNCT
esrj-50337	24	35	.	.	PUNCT
esrj-50337	25	1	as	as	SCONJ
esrj-50337	25	2	a	a	DET
esrj-50337	25	3	clean	clean	ADJ
esrj-50337	25	4	energy	energy	NOUN
esrj-50337	25	5	source	source	NOUN
esrj-50337	25	6	wind	wind	NOUN
esrj-50337	25	7	is	be	AUX
esrj-50337	25	8	considered	consider	VERB
esrj-50337	25	9	an	an	DET
esrj-50337	25	10	alternative	alternative	NOUN
esrj-50337	25	11	to	to	ADP
esrj-50337	25	12	fossil	fossil	ADJ
esrj-50337	25	13	fuels	fuel	NOUN
esrj-50337	25	14	,	,	PUNCT
esrj-50337	25	15	which	which	PRON
esrj-50337	25	16	actually	actually	ADV
esrj-50337	25	17	accelerate	accelerate	VERB
esrj-50337	25	18	global	global	ADJ
esrj-50337	25	19	warming	warming	NOUN
esrj-50337	25	20	.	.	PUNCT
esrj-50337	26	1	the	the	DET
esrj-50337	26	2	first	first	ADJ
esrj-50337	26	3	scientific	scientific	ADJ
esrj-50337	26	4	research	research	NOUN
esrj-50337	26	5	to	to	PART
esrj-50337	26	6	utilize	utilize	VERB
esrj-50337	26	7	wind	wind	NOUN
esrj-50337	26	8	for	for	ADP
esrj-50337	26	9	generating	generate	VERB
esrj-50337	26	10	electricity	electricity	NOUN
esrj-50337	26	11	,	,	PUNCT
esrj-50337	26	12	was	be	AUX
esrj-50337	26	13	initiated	initiate	VERB
esrj-50337	26	14	by	by	ADP
esrj-50337	26	15	the	the	DET
esrj-50337	26	16	danish	danish	NOUN
esrj-50337	26	17	in	in	ADP
esrj-50337	26	18	the	the	DET
esrj-50337	26	19	1960s	1960s	NUM
esrj-50337	26	20	.	.	PUNCT
esrj-50337	27	1	the	the	DET
esrj-50337	27	2	1973	1973	NUM
esrj-50337	27	3	energetic	energetic	ADJ
esrj-50337	27	4	crisis	crisis	NOUN
esrj-50337	27	5	,	,	PUNCT
esrj-50337	27	6	forced	force	VERB
esrj-50337	27	7	many	many	ADJ
esrj-50337	27	8	governments	government	NOUN
esrj-50337	27	9	to	to	PART
esrj-50337	27	10	realize	realize	VERB
esrj-50337	27	11	the	the	DET
esrj-50337	27	12	value	value	NOUN
esrj-50337	27	13	of	of	ADP
esrj-50337	27	14	wind	wind	NOUN
esrj-50337	27	15	,	,	PUNCT
esrj-50337	27	16	as	as	ADP
esrj-50337	27	17	a	a	DET
esrj-50337	27	18	renewable	renewable	ADJ
esrj-50337	27	19	and	and	CCONJ
esrj-50337	27	20	independent	independent	ADJ
esrj-50337	27	21	energy	energy	NOUN
esrj-50337	27	22	source	source	NOUN
esrj-50337	27	23	(	(	PUNCT
esrj-50337	27	24	hanağasioğlu	hanağasioğlu	PROPN
esrj-50337	27	25	,	,	PUNCT
esrj-50337	27	26	1999	1999	NUM
esrj-50337	27	27	)	)	PUNCT
esrj-50337	27	28	.	.	PUNCT
esrj-50337	28	1	electricity	electricity	NOUN
esrj-50337	28	2	generation	generation	NOUN
esrj-50337	28	3	using	use	VERB
esrj-50337	28	4	wind	wind	NOUN
esrj-50337	28	5	energy	energy	NOUN
esrj-50337	28	6	has	have	AUX
esrj-50337	28	7	been	be	AUX
esrj-50337	28	8	well	well	ADV
esrj-50337	28	9	recognized	recognize	VERB
esrj-50337	28	10	as	as	ADP
esrj-50337	28	11	environmentally	environmentally	ADV
esrj-50337	28	12	friendly	friendly	ADJ
esrj-50337	28	13	,	,	PUNCT
esrj-50337	28	14	socially	socially	ADV
esrj-50337	28	15	beneficial	beneficial	ADJ
esrj-50337	28	16	,	,	PUNCT
esrj-50337	28	17	and	and	CCONJ
esrj-50337	28	18	economically	economically	ADV
esrj-50337	28	19	competitive	competitive	ADJ
esrj-50337	28	20	for	for	ADP
esrj-50337	28	21	many	many	ADJ
esrj-50337	28	22	applications	application	NOUN
esrj-50337	28	23	(	(	PUNCT
esrj-50337	28	24	monfared	monfare	VERB
esrj-50337	28	25	et	et	PROPN
esrj-50337	28	26	al	al	PROPN
esrj-50337	28	27	.	.	PROPN
esrj-50337	28	28	,	,	PUNCT
esrj-50337	28	29	2009	2009	NUM
esrj-50337	28	30	)	)	PUNCT
esrj-50337	28	31	.	.	PUNCT
esrj-50337	29	1	prediction	prediction	NOUN
esrj-50337	29	2	of	of	ADP
esrj-50337	29	3	wind	wind	NOUN
esrj-50337	29	4	speed	speed	NOUN
esrj-50337	29	5	(	(	PUNCT
esrj-50337	29	6	ws	ws	NOUN
esrj-50337	29	7	)	)	PUNCT
esrj-50337	29	8	at	at	ADP
esrj-50337	29	9	the	the	DET
esrj-50337	29	10	surface	surface	NOUN
esrj-50337	29	11	or	or	CCONJ
esrj-50337	29	12	near	near	ADP
esrj-50337	29	13	the	the	DET
esrj-50337	29	14	surface	surface	NOUN
esrj-50337	29	15	,	,	PUNCT
esrj-50337	29	16	is	be	AUX
esrj-50337	29	17	essential	essential	ADJ
esrj-50337	29	18	in	in	ADP
esrj-50337	29	19	many	many	ADJ
esrj-50337	29	20	areas	area	NOUN
esrj-50337	29	21	of	of	ADP
esrj-50337	29	22	science	science	NOUN
esrj-50337	29	23	and	and	CCONJ
esrj-50337	29	24	technology	technology	NOUN
esrj-50337	29	25	,	,	PUNCT
esrj-50337	29	26	e.g.	e.g.	ADV
esrj-50337	29	27	,	,	PUNCT
esrj-50337	29	28	wind	wind	NOUN
esrj-50337	29	29	energy	energy	NOUN
esrj-50337	29	30	generation	generation	NOUN
esrj-50337	29	31	,	,	PUNCT
esrj-50337	29	32	aviation	aviation	NOUN
esrj-50337	29	33	,	,	PUNCT
esrj-50337	29	34	space	space	NOUN
esrj-50337	29	35	vehicle	vehicle	NOUN
esrj-50337	29	36	launching	launching	NOUN
esrj-50337	29	37	,	,	PUNCT
esrj-50337	29	38	weather	weather	NOUN
esrj-50337	29	39	forecasting	forecasting	NOUN
esrj-50337	29	40	,	,	PUNCT
esrj-50337	29	41	and	and	CCONJ
esrj-50337	29	42	agro	agro	ADJ
esrj-50337	29	43	-	-	PUNCT
esrj-50337	29	44	meteorology	meteorology	NOUN
esrj-50337	29	45	(	(	PUNCT
esrj-50337	29	46	kulkarni	kulkarni	PROPN
esrj-50337	29	47	et	et	PROPN
esrj-50337	29	48	al	al	PROPN
esrj-50337	29	49	.	.	PROPN
esrj-50337	29	50	,	,	PUNCT
esrj-50337	29	51	2008	2008	NUM
esrj-50337	29	52	)	)	PUNCT
esrj-50337	29	53	.	.	PUNCT
esrj-50337	30	1	wind	wind	NOUN
esrj-50337	30	2	field	field	NOUN
esrj-50337	30	3	prediction	prediction	NOUN
esrj-50337	30	4	at	at	ADP
esrj-50337	30	5	the	the	DET
esrj-50337	30	6	level	level	NOUN
esrj-50337	30	7	of	of	ADP
esrj-50337	30	8	wind	wind	NOUN
esrj-50337	30	9	farm	farm	NOUN
esrj-50337	30	10	,	,	PUNCT
esrj-50337	30	11	is	be	AUX
esrj-50337	30	12	still	still	ADV
esrj-50337	30	13	a	a	DET
esrj-50337	30	14	challenging	challenging	ADJ
esrj-50337	30	15	problem	problem	NOUN
esrj-50337	30	16	.	.	PUNCT
esrj-50337	31	1	different	different	ADJ
esrj-50337	31	2	methods	method	NOUN
esrj-50337	31	3	have	have	AUX
esrj-50337	31	4	been	be	AUX
esrj-50337	31	5	developed	develop	VERB
esrj-50337	31	6	(	(	PUNCT
esrj-50337	31	7	kallos	kallo	NOUN
esrj-50337	31	8	et	et	PROPN
esrj-50337	31	9	al	al	PROPN
esrj-50337	31	10	.	.	PROPN
esrj-50337	31	11	,	,	PUNCT
esrj-50337	31	12	2007	2007	NUM
esrj-50337	31	13	)	)	PUNCT
esrj-50337	31	14	;	;	PUNCT
esrj-50337	31	15	and	and	CCONJ
esrj-50337	31	16	several	several	ADJ
esrj-50337	31	17	studies	study	NOUN
esrj-50337	31	18	have	have	AUX
esrj-50337	31	19	been	be	AUX
esrj-50337	31	20	performed	perform	VERB
esrj-50337	31	21	to	to	PART
esrj-50337	31	22	estimate	estimate	VERB
esrj-50337	31	23	the	the	DET
esrj-50337	31	24	wind	wind	NOUN
esrj-50337	31	25	potential	potential	NOUN
esrj-50337	31	26	in	in	ADP
esrj-50337	31	27	different	different	ADJ
esrj-50337	31	28	parts	part	NOUN
esrj-50337	31	29	of	of	ADP
esrj-50337	31	30	the	the	DET
esrj-50337	31	31	world	world	NOUN
esrj-50337	31	32	(	(	PUNCT
esrj-50337	31	33	çam	çam	NOUN
esrj-50337	31	34	and	and	CCONJ
esrj-50337	31	35	yildiz	yildiz	NOUN
esrj-50337	31	36	,	,	PUNCT
esrj-50337	31	37	2006	2006	NUM
esrj-50337	31	38	)	)	PUNCT
esrj-50337	31	39	.	.	PUNCT
esrj-50337	32	1	there	there	PRON
esrj-50337	32	2	are	be	VERB
esrj-50337	32	3	various	various	ADJ
esrj-50337	32	4	strategies	strategy	NOUN
esrj-50337	32	5	for	for	ADP
esrj-50337	32	6	wind	wind	NOUN
esrj-50337	32	7	speed	speed	NOUN
esrj-50337	32	8	prediction	prediction	NOUN
esrj-50337	32	9	that	that	PRON
esrj-50337	32	10	can	can	AUX
esrj-50337	32	11	be	be	AUX
esrj-50337	32	12	classified	classify	VERB
esrj-50337	32	13	into	into	ADP
esrj-50337	32	14	two	two	NUM
esrj-50337	32	15	categories	category	NOUN
esrj-50337	32	16	:	:	PUNCT
esrj-50337	32	17	(	(	PUNCT
esrj-50337	32	18	1	1	X
esrj-50337	32	19	)	)	PUNCT
esrj-50337	32	20	statistical	statistical	ADJ
esrj-50337	32	21	methods	method	NOUN
esrj-50337	32	22	that	that	PRON
esrj-50337	32	23	can	can	AUX
esrj-50337	32	24	be	be	AUX
esrj-50337	32	25	subdivided	subdivide	VERB
esrj-50337	32	26	into	into	ADP
esrj-50337	32	27	numerical	numerical	ADJ
esrj-50337	32	28	weather	weather	PROPN
esrj-50337	32	29	prediction	prediction	NOUN
esrj-50337	32	30	(	(	PUNCT
esrj-50337	32	31	nwp	nwp	NOUN
esrj-50337	32	32	)	)	PUNCT
esrj-50337	32	33	and	and	CCONJ
esrj-50337	32	34	persistence	persistence	NOUN
esrj-50337	32	35	and	and	CCONJ
esrj-50337	32	36	(	(	PUNCT
esrj-50337	32	37	2	2	NUM
esrj-50337	32	38	)	)	PUNCT
esrj-50337	32	39	artificial	artificial	ADJ
esrj-50337	32	40	intelligence	intelligence	NOUN
esrj-50337	32	41	techniques	technique	NOUN
esrj-50337	32	42	that	that	PRON
esrj-50337	32	43	have	have	VERB
esrj-50337	32	44	subdivisions	subdivision	NOUN
esrj-50337	32	45	such	such	ADJ
esrj-50337	32	46	as	as	ADP
esrj-50337	32	47	artificial	artificial	ADJ
esrj-50337	32	48	neural	neural	ADJ
esrj-50337	32	49	networks	network	NOUN
esrj-50337	32	50	(	(	PUNCT
esrj-50337	32	51	ann	ann	PROPN
esrj-50337	32	52	)	)	PUNCT
esrj-50337	32	53	and	and	CCONJ
esrj-50337	32	54	fuzzy	fuzzy	ADJ
esrj-50337	32	55	logic	logic	NOUN
esrj-50337	32	56	(	(	PUNCT
esrj-50337	32	57	monfared	monfare	VERB
esrj-50337	32	58	et	et	PROPN
esrj-50337	32	59	al	al	PROPN
esrj-50337	32	60	.	.	PROPN
esrj-50337	32	61	,	,	PUNCT
esrj-50337	32	62	2009	2009	NUM
esrj-50337	32	63	)	)	PUNCT
esrj-50337	32	64	.	.	PUNCT
esrj-50337	33	1	with	with	ADP
esrj-50337	33	2	the	the	DET
esrj-50337	33	3	developments	development	NOUN
esrj-50337	33	4	made	make	VERB
esrj-50337	33	5	in	in	ADP
esrj-50337	33	6	chaos	chaos	NOUN
esrj-50337	33	7	theory	theory	NOUN
esrj-50337	33	8	,	,	PUNCT
esrj-50337	33	9	researchers	researcher	NOUN
esrj-50337	33	10	have	have	AUX
esrj-50337	33	11	looked	look	VERB
esrj-50337	33	12	for	for	ADP
esrj-50337	33	13	the	the	DET
esrj-50337	33	14	determinism	determinism	NOUN
esrj-50337	33	15	in	in	ADP
esrj-50337	33	16	various	various	ADJ
esrj-50337	33	17	seemingly	seemingly	ADV
esrj-50337	33	18	chaotic	chaotic	ADJ
esrj-50337	33	19	-	-	PUNCT
esrj-50337	33	20	looking	look	VERB
esrj-50337	33	21	fluctuations	fluctuation	NOUN
esrj-50337	33	22	from	from	ADP
esrj-50337	33	23	different	different	ADJ
esrj-50337	33	24	disciplines	discipline	NOUN
esrj-50337	33	25	such	such	ADJ
esrj-50337	33	26	as	as	ADP
esrj-50337	33	27	physics	physics	NOUN
esrj-50337	33	28	,	,	PUNCT
esrj-50337	33	29	chemistry	chemistry	NOUN
esrj-50337	33	30	,	,	PUNCT
esrj-50337	33	31	hydrology	hydrology	NOUN
esrj-50337	33	32	,	,	PUNCT
esrj-50337	33	33	atmospheric	atmospheric	ADJ
esrj-50337	33	34	sciences	science	NOUN
esrj-50337	33	35	,	,	PUNCT
esrj-50337	33	36	etc	etc	X
esrj-50337	33	37	(	(	PUNCT
esrj-50337	33	38	karunasinghe	karunasinghe	ADJ
esrj-50337	33	39	and	and	CCONJ
esrj-50337	33	40	liong	liong	ADJ
esrj-50337	33	41	,	,	PUNCT
esrj-50337	33	42	2006	2006	NUM
esrj-50337	33	43	)	)	PUNCT
esrj-50337	33	44	.	.	PUNCT
esrj-50337	34	1	the	the	DET
esrj-50337	34	2	time	time	NOUN
esrj-50337	34	3	series	series	PROPN
esrj-50337	34	4	prediction	prediction	NOUN
esrj-50337	34	5	is	be	AUX
esrj-50337	34	6	one	one	NUM
esrj-50337	34	7	of	of	ADP
esrj-50337	34	8	the	the	DET
esrj-50337	34	9	most	most	ADV
esrj-50337	34	10	important	important	ADJ
esrj-50337	34	11	aspects	aspect	NOUN
esrj-50337	34	12	in	in	ADP
esrj-50337	34	13	chaos	chaos	NOUN
esrj-50337	34	14	theory	theory	NOUN
esrj-50337	34	15	.	.	PUNCT
esrj-50337	35	1	time	time	PROPN
esrj-50337	35	2	series	series	PROPN
esrj-50337	35	3	contain	contain	VERB
esrj-50337	35	4	much	much	ADJ
esrj-50337	35	5	information	information	NOUN
esrj-50337	35	6	about	about	ADP
esrj-50337	35	7	dynamic	dynamic	ADJ
esrj-50337	35	8	systems	system	NOUN
esrj-50337	35	9	(	(	PUNCT
esrj-50337	35	10	han	han	PROPN
esrj-50337	35	11	and	and	CCONJ
esrj-50337	35	12	wang	wang	PROPN
esrj-50337	35	13	,	,	PUNCT
esrj-50337	35	14	2009	2009	NUM
esrj-50337	35	15	)	)	PUNCT
esrj-50337	35	16	.	.	PUNCT
esrj-50337	36	1	these	these	DET
esrj-50337	36	2	systems	system	NOUN
esrj-50337	36	3	are	be	AUX
esrj-50337	36	4	usually	usually	ADV
esrj-50337	36	5	modeled	model	VERB
esrj-50337	36	6	by	by	ADP
esrj-50337	36	7	delay	delay	NOUN
esrj-50337	36	8	-	-	PUNCT
esrj-50337	36	9	differential	differential	NOUN
esrj-50337	36	10	equations	equation	NOUN
esrj-50337	36	11	.	.	PUNCT
esrj-50337	37	1	some	some	PRON
esrj-50337	37	2	of	of	ADP
esrj-50337	37	3	them	they	PRON
esrj-50337	37	4	,	,	PUNCT
esrj-50337	37	5	for	for	ADP
esrj-50337	37	6	example	example	NOUN
esrj-50337	37	7	,	,	PUNCT
esrj-50337	37	8	the	the	DET
esrj-50337	37	9	mackey	mackey	PROPN
esrj-50337	37	10	–	–	PUNCT
esrj-50337	37	11	glass	glass	NOUN
esrj-50337	37	12	equation	equation	NOUN
esrj-50337	37	13	(	(	PUNCT
esrj-50337	37	14	mackey	mackey	NOUN
esrj-50337	37	15	and	and	CCONJ
esrj-50337	37	16	glass	glass	NOUN
esrj-50337	37	17	,	,	PUNCT
esrj-50337	37	18	1977	1977	NUM
esrj-50337	37	19	)	)	PUNCT
esrj-50337	37	20	,	,	PUNCT
esrj-50337	37	21	the	the	DET
esrj-50337	37	22	ikeda	ikeda	PROPN
esrj-50337	37	23	equation	equation	NOUN
esrj-50337	37	24	(	(	PUNCT
esrj-50337	37	25	ikeda	ikeda	NOUN
esrj-50337	37	26	,	,	PUNCT
esrj-50337	37	27	1979	1979	NUM
esrj-50337	37	28	)	)	PUNCT
esrj-50337	37	29	,	,	PUNCT
esrj-50337	37	30	and	and	CCONJ
esrj-50337	37	31	equation	equation	NOUN
esrj-50337	37	32	for	for	ADP
esrj-50337	37	33	an	an	DET
esrj-50337	37	34	electronic	electronic	ADJ
esrj-50337	37	35	oscillator	oscillator	NOUN
esrj-50337	37	36	with	with	ADP
esrj-50337	37	37	delayed	delay	VERB
esrj-50337	37	38	feedback	feedback	NOUN
esrj-50337	37	39	(	(	PUNCT
esrj-50337	37	40	chua	chua	PROPN
esrj-50337	37	41	et	et	PROPN
esrj-50337	37	42	al	al	PROPN
esrj-50337	37	43	.	.	PROPN
esrj-50337	37	44	,	,	PUNCT
esrj-50337	37	45	1992	1992	NUM
esrj-50337	37	46	)	)	PUNCT
esrj-50337	37	47	,	,	PUNCT
esrj-50337	37	48	are	be	AUX
esrj-50337	37	49	standard	standard	ADJ
esrj-50337	37	50	examples	example	NOUN
esrj-50337	37	51	of	of	ADP
esrj-50337	37	52	time	time	NOUN
esrj-50337	37	53	-	-	PUNCT
esrj-50337	37	54	delay	delay	NOUN
esrj-50337	37	55	systems	system	NOUN
esrj-50337	37	56	(	(	PUNCT
esrj-50337	37	57	bezruchko	bezruchko	PROPN
esrj-50337	37	58	et	et	PROPN
esrj-50337	37	59	al	al	PROPN
esrj-50337	37	60	.	.	PROPN
esrj-50337	37	61	,	,	PUNCT
esrj-50337	37	62	2001	2001	NUM
esrj-50337	37	63	)	)	PUNCT
esrj-50337	37	64	.	.	PUNCT
esrj-50337	38	1	the	the	DET
esrj-50337	38	2	main	main	ADJ
esrj-50337	38	3	problem	problem	NOUN
esrj-50337	38	4	of	of	ADP
esrj-50337	38	5	the	the	DET
esrj-50337	38	6	time	time	NOUN
esrj-50337	38	7	series	series	PROPN
esrj-50337	38	8	study	study	NOUN
esrj-50337	38	9	consist	consist	NOUN
esrj-50337	38	10	of	of	ADP
esrj-50337	38	11	predicting	predict	VERB
esrj-50337	38	12	the	the	DET
esrj-50337	38	13	next	next	ADJ
esrj-50337	38	14	value	value	NOUN
esrj-50337	38	15	of	of	ADP
esrj-50337	38	16	a	a	DET
esrj-50337	38	17	series	series	NOUN
esrj-50337	38	18	known	know	VERB
esrj-50337	38	19	up	up	ADP
esrj-50337	38	20	to	to	ADP
esrj-50337	38	21	a	a	DET
esrj-50337	38	22	specific	specific	ADJ
esrj-50337	38	23	time	time	NOUN
esrj-50337	38	24	,	,	PUNCT
esrj-50337	38	25	using	use	VERB
esrj-50337	38	26	the	the	DET
esrj-50337	38	27	known	known	ADJ
esrj-50337	38	28	past	past	ADJ
esrj-50337	38	29	values	value	NOUN
esrj-50337	38	30	of	of	ADP
esrj-50337	38	31	the	the	DET
esrj-50337	38	32	series	series	NOUN
esrj-50337	38	33	.	.	PUNCT
esrj-50337	39	1	in	in	ADP
esrj-50337	39	2	time	time	NOUN
esrj-50337	39	3	series	series	PROPN
esrj-50337	39	4	prediction	prediction	PROPN
esrj-50337	39	5	,	,	PUNCT
esrj-50337	39	6	this	this	PRON
esrj-50337	39	7	is	be	AUX
esrj-50337	39	8	usually	usually	ADV
esrj-50337	39	9	first	first	ADV
esrj-50337	39	10	embedded	embed	VERB
esrj-50337	39	11	in	in	ADP
esrj-50337	39	12	a	a	DET
esrj-50337	39	13	state	state	NOUN
esrj-50337	39	14	space	space	NOUN
esrj-50337	39	15	using	use	VERB
esrj-50337	39	16	delay	delay	NOUN
esrj-50337	39	17	coordinates	coordinate	NOUN
esrj-50337	39	18	:	:	PUNCT
esrj-50337	39	19	where	where	SCONJ
esrj-50337	39	20	x(t	x(t	PROPN
esrj-50337	39	21	)	)	PUNCT
esrj-50337	39	22	is	be	AUX
esrj-50337	39	23	the	the	DET
esrj-50337	39	24	value	value	NOUN
esrj-50337	39	25	of	of	ADP
esrj-50337	39	26	the	the	DET
esrj-50337	39	27	time	time	NOUN
esrj-50337	39	28	series	series	NOUN
esrj-50337	39	29	at	at	ADP
esrj-50337	39	30	time	time	NOUN
esrj-50337	39	31	t	t	PROPN
esrj-50337	39	32	,	,	PUNCT
esrj-50337	39	33	τ	τ	PROPN
esrj-50337	39	34	a	a	DET
esrj-50337	39	35	suitable	suitable	ADJ
esrj-50337	39	36	timedelay	timedelay	NOUN
esrj-50337	39	37	and	and	CCONJ
esrj-50337	39	38	d	d	X
esrj-50337	39	39	the	the	DET
esrj-50337	39	40	order	order	NOUN
esrj-50337	39	41	of	of	ADP
esrj-50337	39	42	the	the	DET
esrj-50337	39	43	embedding	embedding	NOUN
esrj-50337	39	44	.	.	PUNCT
esrj-50337	40	1	this	this	DET
esrj-50337	40	2	embedded	embed	VERB
esrj-50337	40	3	vector	vector	NOUN
esrj-50337	40	4	is	be	AUX
esrj-50337	40	5	then	then	ADV
esrj-50337	40	6	used	use	VERB
esrj-50337	40	7	to	to	PART
esrj-50337	40	8	predict	predict	VERB
esrj-50337	40	9	the	the	DET
esrj-50337	40	10	next	next	ADJ
esrj-50337	40	11	value	value	NOUN
esrj-50337	40	12	of	of	ADP
esrj-50337	40	13	the	the	DET
esrj-50337	40	14	series	series	NOUN
esrj-50337	40	15	x(t	x(t	PROPN
esrj-50337	40	16	+	+	CCONJ
esrj-50337	40	17	τ	τ	PROPN
esrj-50337	40	18	)	)	PUNCT
esrj-50337	40	19	.	.	PUNCT
esrj-50337	41	1	therefore	therefore	ADV
esrj-50337	41	2	,	,	PUNCT
esrj-50337	41	3	the	the	DET
esrj-50337	41	4	non	non	ADJ
esrj-50337	41	5	-	-	ADJ
esrj-50337	41	6	linear	linear	ADJ
esrj-50337	41	7	dependence	dependence	NOUN
esrj-50337	41	8	of	of	ADP
esrj-50337	41	9	the	the	DET
esrj-50337	41	10	level	level	NOUN
esrj-50337	41	11	of	of	ADP
esrj-50337	41	12	a	a	DET
esrj-50337	41	13	series	series	NOUN
esrj-50337	41	14	on	on	ADP
esrj-50337	41	15	previous	previous	ADJ
esrj-50337	41	16	data	datum	NOUN
esrj-50337	41	17	points	point	NOUN
esrj-50337	41	18	is	be	AUX
esrj-50337	41	19	of	of	ADP
esrj-50337	41	20	interest	interest	NOUN
esrj-50337	41	21	,	,	PUNCT
esrj-50337	41	22	partly	partly	ADV
esrj-50337	41	23	because	because	SCONJ
esrj-50337	41	24	of	of	ADP
esrj-50337	41	25	the	the	DET
esrj-50337	41	26	possibility	possibility	NOUN
esrj-50337	41	27	of	of	ADP
esrj-50337	41	28	producing	produce	VERB
esrj-50337	41	29	a	a	DET
esrj-50337	41	30	chaotic	chaotic	ADJ
esrj-50337	41	31	time	time	NOUN
esrj-50337	41	32	series	series	NOUN
esrj-50337	41	33	.	.	PUNCT
esrj-50337	42	1	note	note	VERB
esrj-50337	42	2	that	that	SCONJ
esrj-50337	42	3	short	short	ADJ
esrj-50337	42	4	-	-	PUNCT
esrj-50337	42	5	term	term	NOUN
esrj-50337	42	6	prediction	prediction	NOUN
esrj-50337	42	7	for	for	ADP
esrj-50337	42	8	chaotic	chaotic	ADJ
esrj-50337	42	9	time	time	NOUN
esrj-50337	42	10	series	series	NOUN
esrj-50337	42	11	have	have	AUX
esrj-50337	42	12	been	be	AUX
esrj-50337	42	13	widely	widely	ADV
esrj-50337	42	14	investigated	investigate	VERB
esrj-50337	42	15	by	by	ADP
esrj-50337	42	16	several	several	ADJ
esrj-50337	42	17	techniques	technique	NOUN
esrj-50337	42	18	(	(	PUNCT
esrj-50337	42	19	karunasinghe	karunasinghe	ADJ
esrj-50337	42	20	and	and	CCONJ
esrj-50337	42	21	liong	liong	ADJ
esrj-50337	42	22	,	,	PUNCT
esrj-50337	42	23	2006	2006	NUM
esrj-50337	42	24	)	)	PUNCT
esrj-50337	42	25	,	,	PUNCT
esrj-50337	42	26	however	however	ADV
esrj-50337	42	27	,	,	PUNCT
esrj-50337	42	28	the	the	DET
esrj-50337	42	29	long	long	ADJ
esrj-50337	42	30	-	-	PUNCT
esrj-50337	42	31	term	term	NOUN
esrj-50337	42	32	prediction	prediction	NOUN
esrj-50337	42	33	has	have	AUX
esrj-50337	42	34	not	not	PART
esrj-50337	42	35	been	be	AUX
esrj-50337	42	36	widely	widely	ADV
esrj-50337	42	37	studied	study	VERB
esrj-50337	42	38	in	in	ADP
esrj-50337	42	39	the	the	DET
esrj-50337	42	40	literature	literature	NOUN
esrj-50337	42	41	.	.	PUNCT
esrj-50337	43	1	in	in	ADP
esrj-50337	43	2	this	this	DET
esrj-50337	43	3	work	work	NOUN
esrj-50337	43	4	,	,	PUNCT
esrj-50337	43	5	chaotic	chaotic	ADJ
esrj-50337	43	6	time	time	NOUN
esrj-50337	43	7	series	series	PROPN
esrj-50337	43	8	data	datum	NOUN
esrj-50337	43	9	taken	take	VERB
esrj-50337	43	10	from	from	ADP
esrj-50337	43	11	the	the	DET
esrj-50337	43	12	mackey	mackey	PROPN
esrj-50337	43	13	–	–	PUNCT
esrj-50337	43	14	glass	glass	NOUN
esrj-50337	43	15	differential	differential	NOUN
esrj-50337	43	16	equation	equation	NOUN
esrj-50337	43	17	were	be	AUX
esrj-50337	43	18	used	use	VERB
esrj-50337	43	19	to	to	PART
esrj-50337	43	20	develop	develop	VERB
esrj-50337	43	21	a	a	DET
esrj-50337	43	22	neural	neural	ADJ
esrj-50337	43	23	network	network	NOUN
esrj-50337	43	24	.	.	PUNCT
esrj-50337	44	1	in	in	ADP
esrj-50337	44	2	order	order	NOUN
esrj-50337	44	3	to	to	PART
esrj-50337	44	4	still	still	ADV
esrj-50337	44	5	obtain	obtain	VERB
esrj-50337	44	6	a	a	DET
esrj-50337	44	7	more	more	ADV
esrj-50337	44	8	effective	effective	ADJ
esrj-50337	44	9	correlation	correlation	NOUN
esrj-50337	44	10	and	and	CCONJ
esrj-50337	44	11	prediction	prediction	NOUN
esrj-50337	44	12	,	,	PUNCT
esrj-50337	44	13	particle	particle	NOUN
esrj-50337	44	14	swarm	swarm	NOUN
esrj-50337	44	15	algorithm	algorithm	NOUN
esrj-50337	44	16	has	have	AUX
esrj-50337	44	17	been	be	AUX
esrj-50337	44	18	introduced	introduce	VERB
esrj-50337	44	19	to	to	PART
esrj-50337	44	20	update	update	VERB
esrj-50337	44	21	the	the	DET
esrj-50337	44	22	weights	weight	NOUN
esrj-50337	44	23	of	of	ADP
esrj-50337	44	24	all	all	DET
esrj-50337	44	25	layers	layer	NOUN
esrj-50337	44	26	of	of	ADP
esrj-50337	44	27	the	the	DET
esrj-50337	44	28	network	network	NOUN
esrj-50337	44	29	.	.	PUNCT
esrj-50337	45	1	next	next	ADV
esrj-50337	45	2	,	,	PUNCT
esrj-50337	45	3	this	this	DET
esrj-50337	45	4	hybrid	hybrid	ADJ
esrj-50337	45	5	algorithm	algorithm	NOUN
esrj-50337	45	6	was	be	AUX
esrj-50337	45	7	used	use	VERB
esrj-50337	45	8	in	in	ADP
esrj-50337	45	9	the	the	DET
esrj-50337	45	10	long	long	ADJ
esrj-50337	45	11	-	-	PUNCT
esrj-50337	45	12	term	term	NOUN
esrj-50337	45	13	prediction	prediction	NOUN
esrj-50337	45	14	of	of	ADP
esrj-50337	45	15	the	the	DET
esrj-50337	45	16	next	next	ADJ
esrj-50337	45	17	24	24	NUM
esrj-50337	45	18	and	and	CCONJ
esrj-50337	45	19	48	48	NUM
esrj-50337	45	20	hours	hour	NOUN
esrj-50337	45	21	of	of	ADP
esrj-50337	45	22	the	the	DET
esrj-50337	45	23	wind	wind	NOUN
esrj-50337	45	24	speed	speed	NOUN
esrj-50337	45	25	time	time	NOUN
esrj-50337	45	26	series	series	NOUN
esrj-50337	45	27	.	.	PUNCT
esrj-50337	46	1	to	to	ADP
esrj-50337	46	2	the	the	DET
esrj-50337	46	3	best	good	ADJ
esrj-50337	46	4	of	of	ADP
esrj-50337	46	5	the	the	DET
esrj-50337	46	6	authors	author	NOUN
esrj-50337	46	7	’	’	PART
esrj-50337	46	8	knowledge	knowledge	NOUN
esrj-50337	46	9	,	,	PUNCT
esrj-50337	46	10	there	there	PRON
esrj-50337	46	11	is	be	VERB
esrj-50337	46	12	no	no	DET
esrj-50337	46	13	application	application	NOUN
esrj-50337	46	14	for	for	ADP
esrj-50337	46	15	the	the	DET
esrj-50337	46	16	prediction	prediction	NOUN
esrj-50337	46	17	of	of	ADP
esrj-50337	46	18	the	the	DET
esrj-50337	46	19	wind	wind	NOUN
esrj-50337	46	20	speed	speed	NOUN
esrj-50337	46	21	that	that	PRON
esrj-50337	46	22	includes	include	VERB
esrj-50337	46	23	the	the	DET
esrj-50337	46	24	long	long	ADJ
esrj-50337	46	25	-	-	PUNCT
esrj-50337	46	26	term	term	NOUN
esrj-50337	46	27	prediction	prediction	NOUN
esrj-50337	46	28	,	,	PUNCT
esrj-50337	46	29	such	such	ADJ
esrj-50337	46	30	as	as	ADP
esrj-50337	46	31	the	the	DET
esrj-50337	46	32	one	one	NOUN
esrj-50337	46	33	presented	present	VERB
esrj-50337	46	34	here	here	ADV
esrj-50337	46	35	.	.	PUNCT
esrj-50337	47	1	2	2	X
esrj-50337	47	2	.	.	X
esrj-50337	47	3	computational	computational	ADJ
esrj-50337	47	4	method	method	NOUN
esrj-50337	47	5	a	a	DET
esrj-50337	47	6	feed	feed	NOUN
esrj-50337	47	7	-	-	PUNCT
esrj-50337	47	8	forward	forward	NOUN
esrj-50337	47	9	neural	neural	ADJ
esrj-50337	47	10	network	network	NOUN
esrj-50337	47	11	was	be	AUX
esrj-50337	47	12	used	use	VERB
esrj-50337	47	13	to	to	PART
esrj-50337	47	14	represent	represent	VERB
esrj-50337	47	15	non	non	ADJ
esrj-50337	47	16	-	-	ADJ
esrj-50337	47	17	linear	linear	ADJ
esrj-50337	47	18	relationships	relationship	NOUN
esrj-50337	47	19	among	among	ADP
esrj-50337	47	20	variables	variable	NOUN
esrj-50337	47	21	.	.	PUNCT
esrj-50337	48	1	this	this	DET
esrj-50337	48	2	ann	ann	PROPN
esrj-50337	48	3	was	be	AUX
esrj-50337	48	4	implemented	implement	VERB
esrj-50337	48	5	by	by	ADP
esrj-50337	48	6	replacing	replace	VERB
esrj-50337	48	7	standard	standard	ADJ
esrj-50337	48	8	back	back	ADJ
esrj-50337	48	9	-	-	PUNCT
esrj-50337	48	10	propagation	propagation	NOUN
esrj-50337	48	11	algorithm	algorithm	NOUN
esrj-50337	48	12	with	with	ADP
esrj-50337	48	13	particle	particle	NOUN
esrj-50337	48	14	swarm	swarm	NOUN
esrj-50337	48	15	optimization	optimization	NOUN
esrj-50337	48	16	(	(	PUNCT
esrj-50337	48	17	pso	pso	NOUN
esrj-50337	48	18	)	)	PUNCT
esrj-50337	48	19	.	.	PUNCT
esrj-50337	49	1	pso	pso	NOUN
esrj-50337	49	2	is	be	AUX
esrj-50337	49	3	a	a	DET
esrj-50337	49	4	population	population	NOUN
esrj-50337	49	5	-	-	PUNCT
esrj-50337	49	6	based	base	VERB
esrj-50337	49	7	optimization	optimization	NOUN
esrj-50337	49	8	tool	tool	NOUN
esrj-50337	49	9	,	,	PUNCT
esrj-50337	49	10	where	where	SCONJ
esrj-50337	49	11	the	the	DET
esrj-50337	49	12	system	system	NOUN
esrj-50337	49	13	is	be	AUX
esrj-50337	49	14	initialized	initialize	VERB
esrj-50337	49	15	with	with	ADP
esrj-50337	49	16	a	a	DET
esrj-50337	49	17	population	population	NOUN
esrj-50337	49	18	of	of	ADP
esrj-50337	49	19	random	random	ADJ
esrj-50337	49	20	particles	particle	NOUN
esrj-50337	49	21	and	and	CCONJ
esrj-50337	49	22	the	the	DET
esrj-50337	49	23	algorithm	algorithm	NOUN
esrj-50337	49	24	searches	search	VERB
esrj-50337	49	25	for	for	ADP
esrj-50337	49	26	optima	optima	PROPN
esrj-50337	49	27	by	by	ADP
esrj-50337	49	28	updating	update	VERB
esrj-50337	49	29	generations	generation	NOUN
esrj-50337	49	30	(	(	PUNCT
esrj-50337	49	31	eberhart	eberhart	NOUN
esrj-50337	49	32	and	and	CCONJ
esrj-50337	49	33	kennedy	kennedy	PROPN
esrj-50337	49	34	,	,	PUNCT
esrj-50337	49	35	1995	1995	NUM
esrj-50337	49	36	)	)	PUNCT
esrj-50337	49	37	.	.	PUNCT
esrj-50337	50	1	in	in	ADP
esrj-50337	50	2	each	each	DET
esrj-50337	50	3	iteration	iteration	NOUN
esrj-50337	50	4	,	,	PUNCT
esrj-50337	50	5	the	the	DET
esrj-50337	50	6	velocity	velocity	NOUN
esrj-50337	50	7	of	of	ADP
esrj-50337	50	8	each	each	DET
esrj-50337	50	9	particle	particle	NOUN
esrj-50337	50	10	j	j	PROPN
esrj-50337	50	11	is	be	AUX
esrj-50337	50	12	calculated	calculate	VERB
esrj-50337	50	13	according	accord	VERB
esrj-50337	50	14	to	to	ADP
esrj-50337	50	15	the	the	DET
esrj-50337	50	16	following	follow	VERB
esrj-50337	50	17	formula	formula	NOUN
esrj-50337	50	18	(	(	PUNCT
esrj-50337	50	19	lazzús	lazzús	NOUN
esrj-50337	50	20	,	,	PUNCT
esrj-50337	50	21	2011	2011	NUM
esrj-50337	50	22	):	):	PUNCT
esrj-50337	50	23	where	where	SCONJ
esrj-50337	50	24	s	s	PRON
esrj-50337	50	25	and	and	CCONJ
esrj-50337	50	26	v	v	NOUN
esrj-50337	50	27	denote	denote	VERB
esrj-50337	50	28	a	a	DET
esrj-50337	50	29	particle	particle	NOUN
esrj-50337	50	30	position	position	NOUN
esrj-50337	50	31	and	and	CCONJ
esrj-50337	50	32	its	its	PRON
esrj-50337	50	33	corresponding	correspond	VERB
esrj-50337	50	34	velocity	velocity	NOUN
esrj-50337	50	35	in	in	ADP
esrj-50337	50	36	a	a	DET
esrj-50337	50	37	search	search	NOUN
esrj-50337	50	38	space	space	NOUN
esrj-50337	50	39	,	,	PUNCT
esrj-50337	50	40	respectively	respectively	ADV
esrj-50337	50	41	.	.	PUNCT
esrj-50337	51	1	k	k	PROPN
esrj-50337	51	2	is	be	AUX
esrj-50337	51	3	the	the	DET
esrj-50337	51	4	current	current	ADJ
esrj-50337	51	5	step	step	NOUN
esrj-50337	51	6	number	number	NOUN
esrj-50337	51	7	,	,	PUNCT
esrj-50337	51	8	ω	ω	PROPN
esrj-50337	51	9	is	be	AUX
esrj-50337	51	10	the	the	DET
esrj-50337	51	11	inertia	inertia	NOUN
esrj-50337	51	12	weight	weight	NOUN
esrj-50337	51	13	,	,	PUNCT
esrj-50337	51	14	c1	c1	PROPN
esrj-50337	51	15	and	and	CCONJ
esrj-50337	51	16	c2	c2	PROPN
esrj-50337	51	17	are	be	AUX
esrj-50337	51	18	the	the	DET
esrj-50337	51	19	acceleration	acceleration	NOUN
esrj-50337	51	20	constants	constant	NOUN
esrj-50337	51	21	,	,	PUNCT
esrj-50337	51	22	and	and	CCONJ
esrj-50337	51	23	r1	r1	NOUN
esrj-50337	51	24	,	,	PUNCT
esrj-50337	51	25	r2	r2	PROPN
esrj-50337	51	26	are	be	AUX
esrj-50337	51	27	elements	element	NOUN
esrj-50337	51	28	from	from	ADP
esrj-50337	51	29	two	two	NUM
esrj-50337	51	30	random	random	ADJ
esrj-50337	51	31	sequences	sequence	NOUN
esrj-50337	51	32	in	in	ADP
esrj-50337	51	33	the	the	DET
esrj-50337	51	34	range	range	NOUN
esrj-50337	51	35	(	(	PUNCT
esrj-50337	51	36	0,1	0,1	NUM
esrj-50337	51	37	)	)	PUNCT
esrj-50337	51	38	.	.	PUNCT
esrj-50337	52	1	k	k	PROPN
esrj-50337	52	2	js	js	PROPN
esrj-50337	52	3	is	be	AUX
esrj-50337	52	4	the	the	DET
esrj-50337	52	5	current	current	ADJ
esrj-50337	52	6	position	position	NOUN
esrj-50337	52	7	of	of	ADP
esrj-50337	52	8	the	the	DET
esrj-50337	52	9	particle	particle	NOUN
esrj-50337	52	10	,	,	PUNCT
esrj-50337	52	11	ψ	ψ	X
esrj-50337	52	12	k	k	PROPN
esrj-50337	52	13	jy	jy	PROPN
esrj-50337	52	14	is	be	AUX
esrj-50337	52	15	the	the	DET
esrj-50337	52	16	best	good	ADJ
esrj-50337	52	17	one	one	NUM
esrj-50337	52	18	of	of	ADP
esrj-50337	52	19	the	the	DET
esrj-50337	52	20	solutions	solution	NOUN
esrj-50337	52	21	that	that	PRON
esrj-50337	52	22	this	this	DET
esrj-50337	52	23	particle	particle	NOUN
esrj-50337	52	24	has	have	AUX
esrj-50337	52	25	reached	reach	VERB
esrj-50337	52	26	,	,	PUNCT
esrj-50337	52	27	and	and	CCONJ
esrj-50337	52	28	ψg	ψg	PROPN
esrj-50337	52	29	is	be	AUX
esrj-50337	52	30	the	the	DET
esrj-50337	52	31	best	good	ADJ
esrj-50337	52	32	solutions	solution	NOUN
esrj-50337	52	33	that	that	PRON
esrj-50337	52	34	all	all	DET
esrj-50337	52	35	the	the	DET
esrj-50337	52	36	particles	particle	NOUN
esrj-50337	52	37	have	have	AUX
esrj-50337	52	38	reached	reach	VERB
esrj-50337	52	39	.	.	PUNCT
esrj-50337	53	1	in	in	ADP
esrj-50337	53	2	general	general	ADJ
esrj-50337	53	3	,	,	PUNCT
esrj-50337	53	4	the	the	DET
esrj-50337	53	5	value	value	NOUN
esrj-50337	53	6	of	of	ADP
esrj-50337	53	7	each	each	DET
esrj-50337	53	8	component	component	NOUN
esrj-50337	53	9	in	in	ADP
esrj-50337	53	10	v	v	NOUN
esrj-50337	53	11	can	can	AUX
esrj-50337	53	12	be	be	AUX
esrj-50337	53	13	clamped	clamp	VERB
esrj-50337	53	14	to	to	ADP
esrj-50337	53	15	the	the	DET
esrj-50337	53	16	range	range	NOUN
esrj-50337	53	17	[	[	X
esrj-50337	53	18	–	–	PUNCT
esrj-50337	53	19	vmax	vmax	PROPN
esrj-50337	53	20	,	,	PUNCT
esrj-50337	53	21	vmax	vmax	PROPN
esrj-50337	53	22	]	]	PUNCT
esrj-50337	53	23	control	control	PROPN
esrj-50337	53	24	excessive	excessive	ADJ
esrj-50337	53	25	roaming	roaming	NOUN
esrj-50337	53	26	of	of	ADP
esrj-50337	53	27	particles	particle	NOUN
esrj-50337	53	28	outside	outside	ADP
esrj-50337	53	29	the	the	DET
esrj-50337	53	30	search	search	NOUN
esrj-50337	53	31	space	space	NOUN
esrj-50337	53	32	(	(	PUNCT
esrj-50337	53	33	kennedy	kennedy	PROPN
esrj-50337	53	34	et	et	PROPN
esrj-50337	53	35	al	al	PROPN
esrj-50337	53	36	.	.	PROPN
esrj-50337	53	37	,	,	PUNCT
esrj-50337	53	38	2001	2001	NUM
esrj-50337	53	39	)	)	PUNCT
esrj-50337	53	40	.	.	PUNCT
esrj-50337	54	1	after	after	ADP
esrj-50337	54	2	calculating	calculate	VERB
esrj-50337	54	3	the	the	DET
esrj-50337	54	4	velocity	velocity	NOUN
esrj-50337	54	5	,	,	PUNCT
esrj-50337	54	6	the	the	DET
esrj-50337	54	7	new	new	ADJ
esrj-50337	54	8	position	position	NOUN
esrj-50337	54	9	of	of	ADP
esrj-50337	54	10	each	each	DET
esrj-50337	54	11	particle	particle	NOUN
esrj-50337	54	12	is	be	AUX
esrj-50337	54	13	:	:	PUNCT
esrj-50337	54	14	the	the	DET
esrj-50337	54	15	total	total	ADJ
esrj-50337	54	16	steps	step	NOUN
esrj-50337	54	17	to	to	PART
esrj-50337	54	18	calculate	calculate	VERB
esrj-50337	54	19	the	the	DET
esrj-50337	54	20	output	output	NOUN
esrj-50337	54	21	values	value	NOUN
esrj-50337	54	22	,	,	PUNCT
esrj-50337	54	23	using	use	VERB
esrj-50337	54	24	the	the	DET
esrj-50337	54	25	input	input	NOUN
esrj-50337	54	26	values	value	NOUN
esrj-50337	54	27	of	of	ADP
esrj-50337	54	28	the	the	DET
esrj-50337	54	29	network	network	NOUN
esrj-50337	54	30	were	be	AUX
esrj-50337	54	31	as	as	SCONJ
esrj-50337	54	32	follows	follow	VERB
esrj-50337	54	33	(	(	PUNCT
esrj-50337	54	34	lazzús	lazzús	PROPN
esrj-50337	54	35	et	et	PROPN
esrj-50337	54	36	al	al	PROPN
esrj-50337	54	37	.	.	PROPN
esrj-50337	54	38	,	,	PUNCT
esrj-50337	54	39	2014	2014	NUM
esrj-50337	54	40	):	):	PUNCT
esrj-50337	54	41	where	where	SCONJ
esrj-50337	54	42	xi	xi	PROPN
esrj-50337	54	43	is	be	AUX
esrj-50337	54	44	the	the	DET
esrj-50337	54	45	input	input	NOUN
esrj-50337	55	1	variables	variable	NOUN
esrj-50337	55	2	i	i	PRON
esrj-50337	55	3	,	,	PUNCT
esrj-50337	55	4	min	min	PROPN
esrj-50337	55	5	ix	ix	ADJ
esrj-50337	55	6	and	and	CCONJ
esrj-50337	55	7	max	max	PROPN
esrj-50337	55	8	ix	ix	ADV
esrj-50337	55	9	are	be	AUX
esrj-50337	55	10	the	the	DET
esrj-50337	55	11	smallest	small	ADJ
esrj-50337	55	12	and	and	CCONJ
esrj-50337	55	13	largest	large	ADJ
esrj-50337	55	14	value	value	NOUN
esrj-50337	55	15	of	of	ADP
esrj-50337	55	16	the	the	DET
esrj-50337	55	17	data	datum	NOUN
esrj-50337	55	18	,	,	PUNCT
esrj-50337	55	19	thus	thus	ADV
esrj-50337	55	20	the	the	DET
esrj-50337	55	21	input	input	NOUN
esrj-50337	55	22	data	datum	NOUN
esrj-50337	55	23	are	be	AUX
esrj-50337	55	24	normalized	normalize	VERB
esrj-50337	55	25	using	use	VERB
esrj-50337	55	26	this	this	DET
esrj-50337	55	27	equation	equation	NOUN
esrj-50337	55	28	.	.	PUNCT
esrj-50337	56	1	next	next	ADV
esrj-50337	56	2	,	,	PUNCT
esrj-50337	56	3	the	the	DET
esrj-50337	56	4	net	net	ADJ
esrj-50337	56	5	inputs	input	NOUN
esrj-50337	56	6	(	(	PUNCT
esrj-50337	56	7	n	n	CCONJ
esrj-50337	56	8	)	)	PUNCT
esrj-50337	56	9	are	be	AUX
esrj-50337	56	10	calculated	calculate	VERB
esrj-50337	56	11	for	for	ADP
esrj-50337	56	12	the	the	DET
esrj-50337	56	13	hidden	hidden	ADJ
esrj-50337	56	14	neurons	neuron	NOUN
esrj-50337	56	15	coming	come	VERB
esrj-50337	56	16	from	from	ADP
esrj-50337	56	17	the	the	DET
esrj-50337	56	18	inputs	input	NOUN
esrj-50337	56	19	neurons	neuron	NOUN
esrj-50337	56	20	.	.	PUNCT
esrj-50337	57	1	for	for	ADP
esrj-50337	57	2	a	a	DET
esrj-50337	57	3	hidden	hide	VERB
esrj-50337	57	4	neuron	neuron	NOUN
esrj-50337	57	5	:	:	PUNCT
esrj-50337	57	6	where	where	SCONJ
esrj-50337	57	7	pi	pi	NOUN
esrj-50337	57	8	is	be	AUX
esrj-50337	57	9	the	the	DET
esrj-50337	57	10	vector	vector	NOUN
esrj-50337	57	11	of	of	ADP
esrj-50337	57	12	the	the	DET
esrj-50337	57	13	inputs	input	NOUN
esrj-50337	57	14	of	of	ADP
esrj-50337	57	15	the	the	DET
esrj-50337	57	16	training	training	NOUN
esrj-50337	57	17	,	,	PUNCT
esrj-50337	57	18	,	,	PUNCT
esrj-50337	57	19	h	h	NOUN
esrj-50337	57	20	i	i	PRON
esrj-50337	57	21	jw	jw	PROPN
esrj-50337	57	22	is	be	AUX
esrj-50337	57	23	the	the	DET
esrj-50337	57	24	weight	weight	NOUN
esrj-50337	57	25	of	of	ADP
esrj-50337	57	26	the	the	DET
esrj-50337	57	27	connection	connection	NOUN
esrj-50337	57	28	among	among	ADP
esrj-50337	57	29	the	the	DET
esrj-50337	57	30	input	input	NOUN
esrj-50337	57	31	neurons	neuron	NOUN
esrj-50337	57	32	with	with	ADP
esrj-50337	57	33	the	the	DET
esrj-50337	57	34	hidden	hide	VERB
esrj-50337	57	35	layer	layer	NOUN
esrj-50337	57	36	h	h	NOUN
esrj-50337	57	37	,	,	PUNCT
esrj-50337	57	38	and	and	CCONJ
esrj-50337	57	39	the	the	DET
esrj-50337	57	40	term	term	NOUN
esrj-50337	57	41	,	,	PUNCT
esrj-50337	58	1	h	h	NOUN
esrj-50337	58	2	i	i	PRON
esrj-50337	58	3	jb	jb	VERB
esrj-50337	58	4	corresponds	correspond	VERB
esrj-50337	58	5	to	to	ADP
esrj-50337	58	6	the	the	DET
esrj-50337	58	7	bias	bias	NOUN
esrj-50337	58	8	of	of	ADP
esrj-50337	58	9	the	the	DET
esrj-50337	58	10	neuron	neuron	NOUN
esrj-50337	58	11	of	of	ADP
esrj-50337	58	12	the	the	DET
esrj-50337	58	13	hidden	hide	VERB
esrj-50337	58	14	layer	layer	NOUN
esrj-50337	58	15	h	h	NOUN
esrj-50337	58	16	,	,	PUNCT
esrj-50337	58	17	reached	reach	VERB
esrj-50337	58	18	in	in	ADP
esrj-50337	58	19	its	its	PRON
esrj-50337	58	20	activation	activation	NOUN
esrj-50337	58	21	(	(	PUNCT
esrj-50337	58	22	freeman	freeman	NOUN
esrj-50337	58	23	and	and	CCONJ
esrj-50337	58	24	skapura	skapura	NOUN
esrj-50337	58	25	,	,	PUNCT
esrj-50337	58	26	1991	1991	NUM
esrj-50337	58	27	)	)	PUNCT
esrj-50337	58	28	.	.	PUNCT
esrj-50337	59	1	the	the	DET
esrj-50337	59	2	pso	pso	NOUN
esrj-50337	59	3	algorithm	algorithm	NOUN
esrj-50337	59	4	is	be	AUX
esrj-50337	59	5	very	very	ADV
esrj-50337	59	6	different	different	ADJ
esrj-50337	59	7	from	from	ADP
esrj-50337	59	8	any	any	PRON
esrj-50337	59	9	of	of	ADP
esrj-50337	59	10	the	the	DET
esrj-50337	59	11	traditional	traditional	ADJ
esrj-50337	59	12	methods	method	NOUN
esrj-50337	59	13	of	of	ADP
esrj-50337	59	14	training	training	NOUN
esrj-50337	59	15	(	(	PUNCT
esrj-50337	59	16	lazzús	lazzús	NOUN
esrj-50337	59	17	,	,	PUNCT
esrj-50337	59	18	2011	2011	NUM
esrj-50337	59	19	)	)	PUNCT
esrj-50337	59	20	.	.	PUNCT
esrj-50337	60	1	each	each	DET
esrj-50337	60	2	neuron	neuron	NOUN
esrj-50337	60	3	contains	contain	VERB
esrj-50337	60	4	a	a	DET
esrj-50337	60	5	position	position	NOUN
esrj-50337	60	6	and	and	CCONJ
esrj-50337	60	7	velocity	velocity	NOUN
esrj-50337	60	8	.	.	PUNCT
esrj-50337	61	1	the	the	DET
esrj-50337	61	2	position	position	NOUN
esrj-50337	61	3	corresponds	correspond	VERB
esrj-50337	61	4	to	to	ADP
esrj-50337	61	5	the	the	DET
esrj-50337	61	6	weight	weight	NOUN
esrj-50337	61	7	of	of	ADP
esrj-50337	61	8	a	a	DET
esrj-50337	61	9	neuron	neuron	NOUN
esrj-50337	61	10	while	while	SCONJ
esrj-50337	61	11	the	the	DET
esrj-50337	61	12	velocity	velocity	NOUN
esrj-50337	61	13	is	be	AUX
esrj-50337	61	14	used	use	VERB
esrj-50337	61	15	to	to	PART
esrj-50337	61	16	update	update	VERB
esrj-50337	61	17	the	the	DET
esrj-50337	61	18	weight	weight	NOUN
esrj-50337	61	19	.	.	PUNCT
esrj-50337	62	1	starting	start	VERB
esrj-50337	62	2	from	from	ADP
esrj-50337	62	3	these	these	DET
esrj-50337	62	4	inputs	input	NOUN
esrj-50337	62	5	,	,	PUNCT
esrj-50337	62	6	the	the	DET
esrj-50337	62	7	outputs	output	NOUN
esrj-50337	62	8	(	(	PUNCT
esrj-50337	62	9	yi	yi	NOUN
esrj-50337	62	10	)	)	PUNCT
esrj-50337	62	11	of	of	ADP
esrj-50337	62	12	the	the	DET
esrj-50337	62	13	hidden	hide	VERB
esrj-50337	62	14	neurons	neuron	NOUN
esrj-50337	62	15	are	be	AUX
esrj-50337	62	16	calculated	calculate	VERB
esrj-50337	62	17	,	,	PUNCT
esrj-50337	62	18	using	use	VERB
esrj-50337	62	19	a	a	DET
esrj-50337	62	20	transfer	transfer	NOUN
esrj-50337	62	21	function	function	NOUN
esrj-50337	62	22	f	f	PROPN
esrj-50337	62	23	h	h	NOUN
esrj-50337	62	24	associated	associate	VERB
esrj-50337	62	25	with	with	ADP
esrj-50337	62	26	the	the	DET
esrj-50337	62	27	neurons	neuron	NOUN
esrj-50337	62	28	of	of	ADP
esrj-50337	62	29	this	this	DET
esrj-50337	62	30	layer	layer	NOUN
esrj-50337	62	31	(	(	PUNCT
esrj-50337	62	32	freeman	freeman	NOUN
esrj-50337	62	33	and	and	CCONJ
esrj-50337	62	34	skapura	skapura	NOUN
esrj-50337	62	35	,	,	PUNCT
esrj-50337	62	36	1991	1991	NUM
esrj-50337	62	37	)	)	PUNCT
esrj-50337	62	38	.	.	PUNCT
esrj-50337	63	1	to	to	PART
esrj-50337	63	2	minimize	minimize	VERB
esrj-50337	63	3	the	the	DET
esrj-50337	63	4	error	error	NOUN
esrj-50337	63	5	,	,	PUNCT
esrj-50337	63	6	the	the	DET
esrj-50337	63	7	transfer	transfer	NOUN
esrj-50337	63	8	function	function	NOUN
esrj-50337	63	9	f	f	PROPN
esrj-50337	63	10	should	should	AUX
esrj-50337	63	11	be	be	AUX
esrj-50337	63	12	differentiable	differentiable	ADJ
esrj-50337	63	13	.	.	PUNCT
esrj-50337	64	1	in	in	ADP
esrj-50337	64	2	the	the	DET
esrj-50337	64	3	ann	ann	PROPN
esrj-50337	64	4	,	,	PUNCT
esrj-50337	64	5	the	the	DET
esrj-50337	64	6	hyperbolic	hyperbolic	ADJ
esrj-50337	64	7	tangent	tangent	NOUN
esrj-50337	64	8	function	function	NOUN
esrj-50337	64	9	(	(	PUNCT
esrj-50337	64	10	tansig	tansig	NOUN
esrj-50337	64	11	)	)	PUNCT
esrj-50337	64	12	was	be	AUX
esrj-50337	64	13	used	use	VERB
esrj-50337	64	14	as	as	ADP
esrj-50337	64	15	all	all	DET
esrj-50337	64	16	the	the	DET
esrj-50337	64	17	neurons	neuron	NOUN
esrj-50337	64	18	of	of	ADP
esrj-50337	64	19	the	the	DET
esrj-50337	64	20	ann	ann	PROPN
esrj-50337	64	21	have	have	VERB
esrj-50337	64	22	an	an	DET
esrj-50337	64	23	associated	associated	ADJ
esrj-50337	64	24	activation	activation	NOUN
esrj-50337	64	25	value	value	NOUN
esrj-50337	64	26	for	for	ADP
esrj-50337	64	27	a	a	DET
esrj-50337	64	28	given	give	VERB
esrj-50337	64	29	input	input	NOUN
esrj-50337	64	30	pattern	pattern	NOUN
esrj-50337	64	31	;	;	PUNCT
esrj-50337	64	32	the	the	DET
esrj-50337	64	33	algorithm	algorithm	NOUN
esrj-50337	64	34	continues	continue	VERB
esrj-50337	64	35	finding	find	VERB
esrj-50337	64	36	the	the	DET
esrj-50337	64	37	error	error	NOUN
esrj-50337	64	38	that	that	PRON
esrj-50337	64	39	is	be	AUX
esrj-50337	64	40	presented	present	VERB
esrj-50337	64	41	for	for	ADP
esrj-50337	64	42	each	each	DET
esrj-50337	64	43	neuron	neuron	NOUN
esrj-50337	64	44	,	,	PUNCT
esrj-50337	64	45	except	except	SCONJ
esrj-50337	64	46	those	those	PRON
esrj-50337	64	47	of	of	ADP
esrj-50337	64	48	the	the	DET
esrj-50337	64	49	input	input	NOUN
esrj-50337	64	50	layer	layer	NOUN
esrj-50337	64	51	.	.	PUNCT
esrj-50337	65	1	after	after	ADP
esrj-50337	65	2	finding	find	VERB
esrj-50337	65	3	the	the	DET
esrj-50337	65	4	output	output	NOUN
esrj-50337	65	5	values	value	NOUN
esrj-50337	65	6	,	,	PUNCT
esrj-50337	65	7	1	1	NUM
esrj-50337	65	8	1k+	1k+	NUM
esrj-50337	65	9	k	k	PROPN
esrj-50337	65	10	k+	k+	PROPN
esrj-50337	66	1	j	j	PROPN
esrj-50337	66	2	j	j	PROPN
esrj-50337	66	3	js	js	PROPN
esrj-50337	66	4	=	=	SYM
esrj-50337	66	5	s	s	PART
esrj-50337	66	6	+	+	NOUN
esrj-50337	66	7	v	v	ADJ
esrj-50337	66	8	31long	31long	NUM
esrj-50337	66	9	-	-	PUNCT
esrj-50337	66	10	term	term	NOUN
esrj-50337	66	11	prediction	prediction	NOUN
esrj-50337	66	12	of	of	ADP
esrj-50337	66	13	wind	wind	NOUN
esrj-50337	66	14	speed	speed	NOUN
esrj-50337	66	15	in	in	ADP
esrj-50337	66	16	la	la	PRON
esrj-50337	66	17	serena	serena	PROPN
esrj-50337	66	18	city	city	PROPN
esrj-50337	66	19	(	(	PUNCT
esrj-50337	66	20	chile	chile	PROPN
esrj-50337	66	21	)	)	PUNCT
esrj-50337	66	22	using	use	VERB
esrj-50337	66	23	hybrid	hybrid	ADJ
esrj-50337	66	24	neural	neural	ADJ
esrj-50337	66	25	network	network	NOUN
esrj-50337	66	26	-	-	PUNCT
esrj-50337	66	27	particle	particle	NOUN
esrj-50337	66	28	swarm	swarm	NOUN
esrj-50337	66	29	algorithm	algorithm	NOUN
esrj-50337	66	30	the	the	DET
esrj-50337	66	31	weights	weight	NOUN
esrj-50337	66	32	of	of	ADP
esrj-50337	66	33	all	all	DET
esrj-50337	66	34	layers	layer	NOUN
esrj-50337	66	35	of	of	ADP
esrj-50337	66	36	the	the	DET
esrj-50337	66	37	network	network	NOUN
esrj-50337	66	38	are	be	AUX
esrj-50337	66	39	actualized	actualize	VERB
esrj-50337	66	40	by	by	ADP
esrj-50337	66	41	pso	pso	NOUN
esrj-50337	66	42	,	,	PUNCT
esrj-50337	66	43	using	use	VERB
esrj-50337	66	44	equations	equation	NOUN
esrj-50337	66	45	2	2	NUM
esrj-50337	66	46	and	and	CCONJ
esrj-50337	66	47	3	3	NUM
esrj-50337	66	48	(	(	PUNCT
esrj-50337	66	49	lazzús	lazzús	PROPN
esrj-50337	66	50	et	et	PROPN
esrj-50337	66	51	al	al	PROPN
esrj-50337	66	52	.	.	PROPN
esrj-50337	66	53	,	,	PUNCT
esrj-50337	66	54	2014	2014	NUM
esrj-50337	66	55	)	)	PUNCT
esrj-50337	66	56	.	.	PUNCT
esrj-50337	67	1	the	the	DET
esrj-50337	67	2	velocity	velocity	NOUN
esrj-50337	67	3	is	be	AUX
esrj-50337	67	4	used	use	VERB
esrj-50337	67	5	to	to	PART
esrj-50337	67	6	control	control	VERB
esrj-50337	67	7	how	how	SCONJ
esrj-50337	67	8	much	much	ADJ
esrj-50337	67	9	the	the	DET
esrj-50337	67	10	position	position	NOUN
esrj-50337	67	11	is	be	AUX
esrj-50337	67	12	updated	update	VERB
esrj-50337	67	13	.	.	PUNCT
esrj-50337	68	1	on	on	ADP
esrj-50337	68	2	each	each	DET
esrj-50337	68	3	step	step	NOUN
esrj-50337	68	4	,	,	PUNCT
esrj-50337	68	5	pso	pso	NOUN
esrj-50337	68	6	compares	compare	VERB
esrj-50337	68	7	each	each	DET
esrj-50337	68	8	weight	weight	NOUN
esrj-50337	68	9	using	use	VERB
esrj-50337	68	10	the	the	DET
esrj-50337	68	11	data	data	NOUN
esrj-50337	68	12	set	set	VERB
esrj-50337	68	13	.	.	PUNCT
esrj-50337	69	1	the	the	DET
esrj-50337	69	2	network	network	NOUN
esrj-50337	69	3	with	with	ADP
esrj-50337	69	4	the	the	DET
esrj-50337	69	5	highest	high	ADJ
esrj-50337	69	6	fit	fit	ADJ
esrj-50337	69	7	ness	ness	NOUN
esrj-50337	69	8	is	be	AUX
esrj-50337	69	9	considered	consider	VERB
esrj-50337	69	10	the	the	DET
esrj-50337	69	11	global	global	ADJ
esrj-50337	69	12	best	good	ADJ
esrj-50337	69	13	.	.	PUNCT
esrj-50337	70	1	the	the	DET
esrj-50337	70	2	other	other	ADJ
esrj-50337	70	3	weights	weight	NOUN
esrj-50337	70	4	are	be	AUX
esrj-50337	70	5	updated	update	VERB
esrj-50337	70	6	based	base	VERB
esrj-50337	70	7	on	on	ADP
esrj-50337	70	8	the	the	DET
esrj-50337	70	9	global	global	ADJ
esrj-50337	70	10	best	good	ADJ
esrj-50337	70	11	network	network	NOUN
esrj-50337	70	12	rather	rather	ADV
esrj-50337	70	13	than	than	ADP
esrj-50337	70	14	on	on	ADP
esrj-50337	70	15	their	their	PRON
esrj-50337	70	16	personal	personal	ADJ
esrj-50337	70	17	error	error	NOUN
esrj-50337	70	18	or	or	CCONJ
esrj-50337	70	19	fitness	fitness	NOUN
esrj-50337	70	20	(	(	PUNCT
esrj-50337	70	21	pérez	pérez	NOUN
esrj-50337	70	22	ponce	ponce	PROPN
esrj-50337	70	23	et	et	PROPN
esrj-50337	70	24	al	al	PROPN
esrj-50337	70	25	.	.	PROPN
esrj-50337	70	26	,	,	PUNCT
esrj-50337	70	27	2012	2012	NUM
esrj-50337	70	28	;	;	PUNCT
esrj-50337	70	29	lazzús	lazzús	PROPN
esrj-50337	70	30	et	et	PROPN
esrj-50337	70	31	al	al	PROPN
esrj-50337	70	32	.	.	PROPN
esrj-50337	70	33	,	,	PUNCT
esrj-50337	70	34	2014	2014	NUM
esrj-50337	70	35	)	)	PUNCT
esrj-50337	70	36	.	.	PUNCT
esrj-50337	71	1	in	in	ADP
esrj-50337	71	2	this	this	DET
esrj-50337	71	3	article	article	NOUN
esrj-50337	71	4	,	,	PUNCT
esrj-50337	71	5	we	we	PRON
esrj-50337	71	6	used	use	VERB
esrj-50337	71	7	the	the	DET
esrj-50337	71	8	mean	mean	ADJ
esrj-50337	71	9	square	square	NOUN
esrj-50337	71	10	error	error	NOUN
esrj-50337	71	11	(	(	PUNCT
esrj-50337	71	12	mse	mse	NOUN
esrj-50337	71	13	)	)	PUNCT
esrj-50337	71	14	to	to	PART
esrj-50337	71	15	determine	determine	VERB
esrj-50337	71	16	network	network	NOUN
esrj-50337	71	17	fitness	fitness	NOUN
esrj-50337	71	18	for	for	ADP
esrj-50337	71	19	the	the	DET
esrj-50337	71	20	entire	entire	ADJ
esrj-50337	71	21	training	training	NOUN
esrj-50337	71	22	set	set	NOUN
esrj-50337	71	23	:	:	PUNCT
esrj-50337	71	24	where	where	SCONJ
esrj-50337	71	25	yi	yi	PROPN
esrj-50337	71	26	is	be	AUX
esrj-50337	71	27	the	the	DET
esrj-50337	71	28	output	output	NOUN
esrj-50337	71	29	value	value	NOUN
esrj-50337	71	30	obtained	obtain	VERB
esrj-50337	71	31	from	from	ADP
esrj-50337	71	32	the	the	DET
esrj-50337	71	33	normalized	normalize	VERB
esrj-50337	71	34	output	output	NOUN
esrj-50337	71	35	(	(	PUNCT
esrj-50337	71	36	yi	yi	NOUN
esrj-50337	71	37	)	)	PUNCT
esrj-50337	71	38	of	of	ADP
esrj-50337	71	39	the	the	DET
esrj-50337	71	40	network	network	NOUN
esrj-50337	71	41	.	.	PUNCT
esrj-50337	72	1	this	this	DET
esrj-50337	72	2	process	process	NOUN
esrj-50337	72	3	was	be	AUX
esrj-50337	72	4	repeated	repeat	VERB
esrj-50337	72	5	for	for	ADP
esrj-50337	72	6	the	the	DET
esrj-50337	72	7	total	total	ADJ
esrj-50337	72	8	number	number	NOUN
esrj-50337	72	9	of	of	ADP
esrj-50337	72	10	patterns	pattern	NOUN
esrj-50337	72	11	to	to	ADP
esrj-50337	72	12	training	training	NOUN
esrj-50337	72	13	.	.	PUNCT
esrj-50337	73	1	for	for	ADP
esrj-50337	73	2	a	a	DET
esrj-50337	73	3	successful	successful	ADJ
esrj-50337	73	4	process	process	NOUN
esrj-50337	73	5	the	the	DET
esrj-50337	73	6	objective	objective	NOUN
esrj-50337	73	7	of	of	ADP
esrj-50337	73	8	the	the	DET
esrj-50337	73	9	algorithm	algorithm	NOUN
esrj-50337	73	10	is	be	AUX
esrj-50337	73	11	to	to	PART
esrj-50337	73	12	modernize	modernize	VERB
esrj-50337	73	13	all	all	DET
esrj-50337	73	14	the	the	DET
esrj-50337	73	15	weights	weight	NOUN
esrj-50337	73	16	by	by	ADP
esrj-50337	73	17	minimizing	minimize	VERB
esrj-50337	73	18	the	the	DET
esrj-50337	73	19	total	total	ADJ
esrj-50337	73	20	root	root	NOUN
esrj-50337	73	21	mean	mean	VERB
esrj-50337	73	22	squared	square	VERB
esrj-50337	73	23	error	error	NOUN
esrj-50337	73	24	(	(	PUNCT
esrj-50337	73	25	rmse	rmse	ADJ
esrj-50337	73	26	):	):	PUNCT
esrj-50337	73	27	figure	figure	NOUN
esrj-50337	73	28	1	1	NUM
esrj-50337	73	29	presents	present	VERB
esrj-50337	73	30	a	a	DET
esrj-50337	73	31	block	block	NOUN
esrj-50337	73	32	diagram	diagram	NOUN
esrj-50337	73	33	of	of	ADP
esrj-50337	73	34	the	the	DET
esrj-50337	73	35	ann+pso	ann+pso	PROPN
esrj-50337	73	36	algorithm	algorithm	NOUN
esrj-50337	73	37	developed	develop	VERB
esrj-50337	73	38	in	in	ADP
esrj-50337	73	39	this	this	DET
esrj-50337	73	40	study	study	NOUN
esrj-50337	73	41	.	.	PUNCT
esrj-50337	74	1	in	in	ADP
esrj-50337	74	2	pso	pso	NOUN
esrj-50337	74	3	,	,	PUNCT
esrj-50337	74	4	the	the	DET
esrj-50337	74	5	inertial	inertial	ADJ
esrj-50337	74	6	weight	weight	NOUN
esrj-50337	74	7	ω	ω	PROPN
esrj-50337	74	8	,	,	PUNCT
esrj-50337	74	9	the	the	DET
esrj-50337	74	10	constant	constant	ADJ
esrj-50337	74	11	c1	c1	NOUN
esrj-50337	74	12	and	and	CCONJ
esrj-50337	74	13	c2	c2	PROPN
esrj-50337	74	14	,	,	PUNCT
esrj-50337	74	15	the	the	DET
esrj-50337	74	16	number	number	NOUN
esrj-50337	74	17	of	of	ADP
esrj-50337	74	18	particles	particle	NOUN
esrj-50337	74	19	npart	npart	NOUN
esrj-50337	74	20	and	and	CCONJ
esrj-50337	74	21	the	the	DET
esrj-50337	74	22	maximum	maximum	ADJ
esrj-50337	74	23	speed	speed	NOUN
esrj-50337	74	24	of	of	ADP
esrj-50337	74	25	particle	particle	NOUN
esrj-50337	74	26	summarizes	summarize	NOUN
esrj-50337	74	27	the	the	DET
esrj-50337	74	28	parameters	parameter	NOUN
esrj-50337	74	29	to	to	PART
esrj-50337	74	30	synchronize	synchronize	VERB
esrj-50337	74	31	for	for	ADP
esrj-50337	74	32	their	their	PRON
esrj-50337	74	33	application	application	NOUN
esrj-50337	74	34	in	in	ADP
esrj-50337	74	35	a	a	DET
esrj-50337	74	36	given	give	VERB
esrj-50337	74	37	problem	problem	NOUN
esrj-50337	74	38	.	.	PUNCT
esrj-50337	75	1	an	an	DET
esrj-50337	75	2	exhaustive	exhaustive	ADJ
esrj-50337	75	3	trial	trial	NOUN
esrj-50337	75	4	-	-	PUNCT
esrj-50337	75	5	anderror	anderror	NOUN
esrj-50337	75	6	procedure	procedure	NOUN
esrj-50337	75	7	was	be	AUX
esrj-50337	75	8	applied	apply	VERB
esrj-50337	75	9	for	for	ADP
esrj-50337	75	10	tuning	tune	VERB
esrj-50337	75	11	the	the	DET
esrj-50337	75	12	pso	pso	NOUN
esrj-50337	75	13	parameters	parameter	NOUN
esrj-50337	75	14	.	.	PUNCT
esrj-50337	76	1	table	table	NOUN
esrj-50337	76	2	1	1	NUM
esrj-50337	76	3	shows	show	VERB
esrj-50337	76	4	the	the	DET
esrj-50337	76	5	selected	select	VERB
esrj-50337	76	6	parameters	parameter	NOUN
esrj-50337	76	7	for	for	ADP
esrj-50337	76	8	this	this	DET
esrj-50337	76	9	hybrid	hybrid	ADJ
esrj-50337	76	10	algorithm	algorithm	NOUN
esrj-50337	76	11	.	.	PUNCT
esrj-50337	77	1	3	3	X
esrj-50337	77	2	.	.	X
esrj-50337	77	3	simulations	simulation	NOUN
esrj-50337	77	4	3.1	3.1	NUM
esrj-50337	77	5	.	.	PUNCT
esrj-50337	77	6	mackey	mackey	NOUN
esrj-50337	77	7	-	-	PUNCT
esrj-50337	77	8	glass	glass	NOUN
esrj-50337	77	9	time	time	NOUN
esrj-50337	77	10	series	series	NOUN
esrj-50337	77	11	to	to	PART
esrj-50337	77	12	evaluate	evaluate	VERB
esrj-50337	77	13	the	the	DET
esrj-50337	77	14	capability	capability	NOUN
esrj-50337	77	15	of	of	ADP
esrj-50337	77	16	the	the	DET
esrj-50337	77	17	proposed	propose	VERB
esrj-50337	77	18	hybrid	hybrid	ADJ
esrj-50337	77	19	algorithm	algorithm	NOUN
esrj-50337	77	20	in	in	ADP
esrj-50337	77	21	the	the	DET
esrj-50337	77	22	long	long	ADJ
esrj-50337	77	23	-	-	PUNCT
esrj-50337	77	24	term	term	NOUN
esrj-50337	77	25	prediction	prediction	NOUN
esrj-50337	77	26	,	,	PUNCT
esrj-50337	77	27	the	the	DET
esrj-50337	77	28	mackey	mackey	PROPN
esrj-50337	77	29	–	–	PUNCT
esrj-50337	77	30	glass	glass	NOUN
esrj-50337	77	31	time	time	NOUN
esrj-50337	77	32	series	series	NOUN
esrj-50337	77	33	was	be	AUX
esrj-50337	77	34	used	use	VERB
esrj-50337	77	35	.	.	PUNCT
esrj-50337	78	1	thus	thus	ADV
esrj-50337	78	2	,	,	PUNCT
esrj-50337	78	3	a	a	DET
esrj-50337	78	4	set	set	NOUN
esrj-50337	78	5	of	of	ADP
esrj-50337	78	6	data	datum	NOUN
esrj-50337	78	7	points	point	NOUN
esrj-50337	78	8	were	be	AUX
esrj-50337	78	9	generated	generate	VERB
esrj-50337	78	10	from	from	ADP
esrj-50337	78	11	the	the	DET
esrj-50337	78	12	mackey	mackey	PROPN
esrj-50337	78	13	–	–	PUNCT
esrj-50337	78	14	glass	glass	NOUN
esrj-50337	78	15	time	time	NOUN
esrj-50337	78	16	-	-	PUNCT
esrj-50337	78	17	delay	delay	NOUN
esrj-50337	78	18	differential	differential	ADJ
esrj-50337	78	19	equation	equation	NOUN
esrj-50337	78	20	(	(	PUNCT
esrj-50337	78	21	mackey	mackey	NOUN
esrj-50337	78	22	and	and	CCONJ
esrj-50337	78	23	glass	glass	NOUN
esrj-50337	78	24	,	,	PUNCT
esrj-50337	78	25	1977	1977	NUM
esrj-50337	78	26	;	;	PUNCT
esrj-50337	78	27	farmer	farmer	NOUN
esrj-50337	78	28	,	,	PUNCT
esrj-50337	78	29	1982	1982	NUM
esrj-50337	78	30	)	)	PUNCT
esrj-50337	78	31	which	which	PRON
esrj-50337	78	32	is	be	AUX
esrj-50337	78	33	defined	define	VERB
esrj-50337	78	34	by	by	ADP
esrj-50337	78	35	:	:	PUNCT
esrj-50337	78	36	(	(	PUNCT
esrj-50337	78	37	11	11	NUM
esrj-50337	78	38	)	)	PUNCT
esrj-50337	78	39	where	where	SCONJ
esrj-50337	78	40	t	t	PROPN
esrj-50337	78	41	is	be	AUX
esrj-50337	78	42	a	a	DET
esrj-50337	78	43	variable	variable	NOUN
esrj-50337	78	44	,	,	PUNCT
esrj-50337	78	45	x	x	PUNCT
esrj-50337	78	46	is	be	AUX
esrj-50337	78	47	a	a	DET
esrj-50337	78	48	function	function	NOUN
esrj-50337	78	49	of	of	ADP
esrj-50337	78	50	t	t	PROPN
esrj-50337	78	51	,	,	PUNCT
esrj-50337	78	52	and	and	CCONJ
esrj-50337	78	53	τ	τ	PROPN
esrj-50337	78	54	is	be	AUX
esrj-50337	78	55	the	the	DET
esrj-50337	78	56	time	time	NOUN
esrj-50337	78	57	delay	delay	NOUN
esrj-50337	78	58	.	.	PUNCT
esrj-50337	79	1	the	the	DET
esrj-50337	79	2	initial	initial	ADJ
esrj-50337	79	3	values	value	NOUN
esrj-50337	79	4	of	of	ADP
esrj-50337	79	5	the	the	DET
esrj-50337	79	6	time	time	NOUN
esrj-50337	79	7	series	series	PROPN
esrj-50337	79	8	are	be	AUX
esrj-50337	79	9	α	α	NOUN
esrj-50337	79	10	=	=	ADJ
esrj-50337	79	11	0.2	0.2	NUM
esrj-50337	79	12	,	,	PUNCT
esrj-50337	79	13	β	β	X
esrj-50337	79	14	=	=	NOUN
esrj-50337	79	15	0.1	0.1	NUM
esrj-50337	79	16	,	,	PUNCT
esrj-50337	79	17	and	and	CCONJ
esrj-50337	79	18	x(0)=1.2	x(0)=1.2	NOUN
esrj-50337	79	19	.	.	PROPN
esrj-50337	80	1	if	if	SCONJ
esrj-50337	80	2	τ	τ	PROPN
esrj-50337	80	3	≥	≥	NOUN
esrj-50337	80	4	17	17	NUM
esrj-50337	80	5	,	,	PUNCT
esrj-50337	80	6	the	the	DET
esrj-50337	80	7	time	time	NOUN
esrj-50337	80	8	series	series	PROPN
esrj-50337	80	9	show	show	VERB
esrj-50337	80	10	the	the	DET
esrj-50337	80	11	chaotic	chaotic	ADJ
esrj-50337	80	12	behaviour	behaviour	NOUN
esrj-50337	80	13	(	(	PUNCT
esrj-50337	80	14	farmer	farmer	NOUN
esrj-50337	80	15	,	,	PUNCT
esrj-50337	80	16	1982	1982	NUM
esrj-50337	80	17	;	;	PUNCT
esrj-50337	80	18	mirzaee	mirzaee	PROPN
esrj-50337	80	19	,	,	PUNCT
esrj-50337	80	20	2009	2009	NUM
esrj-50337	80	21	)	)	PUNCT
esrj-50337	80	22	.	.	PUNCT
esrj-50337	81	1	the	the	DET
esrj-50337	81	2	goal	goal	NOUN
esrj-50337	81	3	of	of	ADP
esrj-50337	81	4	the	the	DET
esrj-50337	81	5	task	task	NOUN
esrj-50337	81	6	is	be	AUX
esrj-50337	81	7	to	to	PART
esrj-50337	81	8	use	use	VERB
esrj-50337	81	9	known	known	ADJ
esrj-50337	81	10	values	value	NOUN
esrj-50337	81	11	of	of	ADP
esrj-50337	81	12	the	the	DET
esrj-50337	81	13	time	time	NOUN
esrj-50337	81	14	series	series	NOUN
esrj-50337	81	15	up	up	ADP
esrj-50337	81	16	to	to	ADP
esrj-50337	81	17	the	the	DET
esrj-50337	81	18	point	point	NOUN
esrj-50337	81	19	x	x	NOUN
esrj-50337	81	20	=	=	NOUN
esrj-50337	81	21	t	t	X
esrj-50337	81	22	to	to	PART
esrj-50337	81	23	predict	predict	VERB
esrj-50337	81	24	the	the	DET
esrj-50337	81	25	value	value	NOUN
esrj-50337	81	26	at	at	ADP
esrj-50337	81	27	some	some	DET
esrj-50337	81	28	point	point	NOUN
esrj-50337	81	29	in	in	ADP
esrj-50337	81	30	the	the	DET
esrj-50337	81	31	future	future	NOUN
esrj-50337	81	32	x	x	X
esrj-50337	81	33	=	=	X
esrj-50337	81	34	t+t	t+t	PROPN
esrj-50337	81	35	.	.	PUNCT
esrj-50337	82	1	the	the	DET
esrj-50337	82	2	standard	standard	ADJ
esrj-50337	82	3	method	method	NOUN
esrj-50337	82	4	for	for	ADP
esrj-50337	82	5	this	this	DET
esrj-50337	82	6	type	type	NOUN
esrj-50337	82	7	of	of	ADP
esrj-50337	82	8	prediction	prediction	NOUN
esrj-50337	82	9	is	be	AUX
esrj-50337	82	10	to	to	PART
esrj-50337	82	11	create	create	VERB
esrj-50337	82	12	a	a	DET
esrj-50337	82	13	mapping	mapping	NOUN
esrj-50337	82	14	from	from	ADP
esrj-50337	82	15	d	d	PROPN
esrj-50337	82	16	points	point	NOUN
esrj-50337	82	17	of	of	ADP
esrj-50337	82	18	the	the	DET
esrj-50337	82	19	time	time	NOUN
esrj-50337	82	20	series	series	PROPN
esrj-50337	82	21	spaced	space	VERB
esrj-50337	82	22	apart	apart	ADV
esrj-50337	82	23	,	,	PUNCT
esrj-50337	82	24	that	that	ADV
esrj-50337	82	25	is	is	ADV
esrj-50337	82	26	,	,	PUNCT
esrj-50337	82	27	to	to	ADP
esrj-50337	82	28	a	a	DET
esrj-50337	82	29	predicted	predict	VERB
esrj-50337	82	30	future	future	ADJ
esrj-50337	82	31	value	value	NOUN
esrj-50337	82	32	x(t+t	x(t+t	PROPN
esrj-50337	82	33	)	)	PUNCT
esrj-50337	82	34	.	.	PUNCT
esrj-50337	83	1	in	in	ADP
esrj-50337	83	2	order	order	NOUN
esrj-50337	83	3	to	to	PART
esrj-50337	83	4	solve	solve	VERB
esrj-50337	83	5	the	the	DET
esrj-50337	83	6	mackey	mackey	PROPN
esrj-50337	83	7	–	–	PUNCT
esrj-50337	83	8	glass	glass	NOUN
esrj-50337	83	9	equation	equation	NOUN
esrj-50337	83	10	,	,	PUNCT
esrj-50337	83	11	the	the	DET
esrj-50337	83	12	fourth	fourth	ADJ
esrj-50337	83	13	-	-	PUNCT
esrj-50337	83	14	order	order	NOUN
esrj-50337	83	15	runge	runge	NOUN
esrj-50337	83	16	–	–	PUNCT
esrj-50337	83	17	kutta	kutta	NOUN
esrj-50337	83	18	method	method	NOUN
esrj-50337	83	19	was	be	AUX
esrj-50337	83	20	applied	apply	VERB
esrj-50337	83	21	to	to	PART
esrj-50337	83	22	find	find	VERB
esrj-50337	83	23	the	the	DET
esrj-50337	83	24	numerical	numerical	ADJ
esrj-50337	83	25	solution	solution	NOUN
esrj-50337	83	26	.	.	PUNCT
esrj-50337	84	1	the	the	DET
esrj-50337	84	2	time	time	NOUN
esrj-50337	84	3	series	series	PROPN
esrj-50337	84	4	was	be	AUX
esrj-50337	84	5	obtained	obtain	VERB
esrj-50337	84	6	evaluating	evaluate	VERB
esrj-50337	84	7	the	the	DET
esrj-50337	84	8	solution	solution	NOUN
esrj-50337	84	9	of	of	ADP
esrj-50337	84	10	eq	eq	PROPN
esrj-50337	84	11	.	.	PUNCT
esrj-50337	85	1	(	(	PUNCT
esrj-50337	85	2	11	11	NUM
esrj-50337	85	3	)	)	PUNCT
esrj-50337	85	4	at	at	ADP
esrj-50337	85	5	each	each	DET
esrj-50337	85	6	integer	integer	NOUN
esrj-50337	85	7	points	point	NOUN
esrj-50337	85	8	.	.	PUNCT
esrj-50337	86	1	step	step	NOUN
esrj-50337	86	2	size	size	NOUN
esrj-50337	86	3	of	of	ADP
esrj-50337	86	4	0.1	0.1	NUM
esrj-50337	86	5	was	be	AUX
esrj-50337	86	6	used	use	VERB
esrj-50337	86	7	to	to	PART
esrj-50337	86	8	generate	generate	VERB
esrj-50337	86	9	a	a	DET
esrj-50337	86	10	time	time	NOUN
esrj-50337	86	11	series	series	NOUN
esrj-50337	86	12	,	,	PUNCT
esrj-50337	86	13	and	and	CCONJ
esrj-50337	86	14	x(t	x(t	PROPN
esrj-50337	86	15	)	)	PUNCT
esrj-50337	86	16	is	be	AUX
esrj-50337	86	17	thus	thus	ADV
esrj-50337	86	18	derived	derive	VERB
esrj-50337	86	19	for	for	ADP
esrj-50337	86	20	with	with	ADP
esrj-50337	86	21	x(t)=0	x(t)=0	NOUN
esrj-50337	86	22	for	for	ADP
esrj-50337	86	23	t	t	NOUN
esrj-50337	86	24	<	<	X
esrj-50337	86	25	0	0	NUM
esrj-50337	86	26	in	in	ADP
esrj-50337	86	27	the	the	DET
esrj-50337	86	28	integration	integration	NOUN
esrj-50337	86	29	.	.	PUNCT
esrj-50337	87	1	four	four	NUM
esrj-50337	87	2	non	non	X
esrj-50337	87	3	consecutive	consecutive	ADJ
esrj-50337	87	4	points	point	NOUN
esrj-50337	87	5	in	in	ADP
esrj-50337	87	6	the	the	DET
esrj-50337	87	7	time	time	NOUN
esrj-50337	87	8	series	series	NOUN
esrj-50337	87	9	are	be	AUX
esrj-50337	87	10	given	give	VERB
esrj-50337	87	11	to	to	PART
esrj-50337	87	12	generate	generate	VERB
esrj-50337	87	13	each	each	DET
esrj-50337	87	14	input	input	NOUN
esrj-50337	87	15	vector	vector	NOUN
esrj-50337	87	16	xi	xi	X
esrj-50337	87	17	(	(	PUNCT
esrj-50337	87	18	where	where	SCONJ
esrj-50337	87	19	i=1	i=1	PROPN
esrj-50337	87	20	,	,	PUNCT
esrj-50337	87	21	2	2	NUM
esrj-50337	87	22	,	,	PUNCT
esrj-50337	87	23	…	…	PUNCT
esrj-50337	87	24	,	,	PUNCT
esrj-50337	87	25	n	n	CCONJ
esrj-50337	87	26	)	)	PUNCT
esrj-50337	87	27	of	of	ADP
esrj-50337	87	28	the	the	DET
esrj-50337	87	29	input	input	NOUN
esrj-50337	87	30	matrix	matrix	NOUN
esrj-50337	87	31	x	x	X
esrj-50337	87	32	,	,	PUNCT
esrj-50337	87	33	as	as	SCONJ
esrj-50337	87	34	:	:	PUNCT
esrj-50337	87	35	(	(	PUNCT
esrj-50337	87	36	12	12	NUM
esrj-50337	87	37	)	)	PUNCT
esrj-50337	87	38	a	a	DET
esrj-50337	87	39	similar	similar	ADJ
esrj-50337	87	40	criterion	criterion	NOUN
esrj-50337	87	41	was	be	AUX
esrj-50337	87	42	used	use	VERB
esrj-50337	87	43	to	to	PART
esrj-50337	87	44	create	create	VERB
esrj-50337	87	45	the	the	DET
esrj-50337	87	46	output	output	NOUN
esrj-50337	87	47	matrix	matrix	NOUN
esrj-50337	87	48	,	,	PUNCT
esrj-50337	87	49	as	as	ADP
esrj-50337	87	50	:	:	PUNCT
esrj-50337	87	51	(	(	PUNCT
esrj-50337	87	52	13	13	X
esrj-50337	87	53	)	)	PUNCT
esrj-50337	87	54	figure	figure	NOUN
esrj-50337	87	55	1	1	NUM
esrj-50337	87	56	.	.	PUNCT
esrj-50337	87	57	flow	flow	NOUN
esrj-50337	87	58	diagram	diagram	NOUN
esrj-50337	87	59	for	for	ADP
esrj-50337	87	60	training	training	NOUN
esrj-50337	87	61	of	of	ADP
esrj-50337	87	62	the	the	DET
esrj-50337	87	63	ann	ann	PROPN
esrj-50337	87	64	using	use	VERB
esrj-50337	87	65	pso	pso	NOUN
esrj-50337	87	66	algorithm	algorithm	NOUN
esrj-50337	87	67	table	table	NOUN
esrj-50337	87	68	1	1	NUM
esrj-50337	87	69	.	.	PUNCT
esrj-50337	88	1	parameters	parameter	NOUN
esrj-50337	88	2	used	use	VERB
esrj-50337	88	3	in	in	ADP
esrj-50337	88	4	the	the	DET
esrj-50337	88	5	hybrid	hybrid	ADJ
esrj-50337	88	6	ann+pso	ann+pso	NOUN
esrj-50337	88	7	algorithm	algorithm	NOUN
esrj-50337	88	8	.	.	PUNCT
esrj-50337	89	1	32	32	NUM
esrj-50337	89	2	juan	juan	PROPN
esrj-50337	89	3	a.	a.	PROPN
esrj-50337	89	4	lazzús	lazzús	PROPN
esrj-50337	89	5	and	and	CCONJ
esrj-50337	89	6	ignacio	ignacio	PROPN
esrj-50337	89	7	salfate	salfate	VERB
esrj-50337	89	8	then	then	ADV
esrj-50337	89	9	,	,	PUNCT
esrj-50337	89	10	the	the	DET
esrj-50337	89	11	ann+pso	ann+pso	PROPN
esrj-50337	89	12	method	method	NOUN
esrj-50337	89	13	was	be	AUX
esrj-50337	89	14	used	use	VERB
esrj-50337	89	15	in	in	ADP
esrj-50337	89	16	the	the	DET
esrj-50337	89	17	long	long	ADJ
esrj-50337	89	18	-	-	PUNCT
esrj-50337	89	19	term	term	NOUN
esrj-50337	89	20	prediction	prediction	NOUN
esrj-50337	89	21	.	.	PUNCT
esrj-50337	90	1	this	this	DET
esrj-50337	90	2	hybrid	hybrid	ADJ
esrj-50337	90	3	algorithm	algorithm	NOUN
esrj-50337	90	4	was	be	AUX
esrj-50337	90	5	trained	train	VERB
esrj-50337	90	6	to	to	PART
esrj-50337	90	7	predict	predict	VERB
esrj-50337	90	8	the	the	DET
esrj-50337	90	9	future	future	ADJ
esrj-50337	90	10	value	value	NOUN
esrj-50337	90	11	x(t+84	x(t+84	PROPN
esrj-50337	90	12	)	)	PUNCT
esrj-50337	90	13	from	from	ADP
esrj-50337	90	14	the	the	DET
esrj-50337	90	15	current	current	ADJ
esrj-50337	90	16	value	value	NOUN
esrj-50337	90	17	x(t	x(t	PROPN
esrj-50337	90	18	)	)	PUNCT
esrj-50337	90	19	and	and	CCONJ
esrj-50337	90	20	the	the	DET
esrj-50337	90	21	past	past	ADJ
esrj-50337	90	22	values	value	NOUN
esrj-50337	90	23	,	,	PUNCT
esrj-50337	90	24	using	use	VERB
esrj-50337	90	25	the	the	DET
esrj-50337	90	26	standard	standard	ADJ
esrj-50337	90	27	form	form	NOUN
esrj-50337	90	28	applied	apply	VERB
esrj-50337	90	29	in	in	ADP
esrj-50337	90	30	the	the	DET
esrj-50337	90	31	literature	literature	NOUN
esrj-50337	90	32	,	,	PUNCT
esrj-50337	90	33	for	for	ADP
esrj-50337	90	34	d=4	d=4	PROPN
esrj-50337	90	35	and	and	CCONJ
esrj-50337	90	36	∆=t=6	∆=t=6	PROPN
esrj-50337	90	37	(	(	PUNCT
esrj-50337	90	38	chng	chng	PROPN
esrj-50337	90	39	et	et	PROPN
esrj-50337	90	40	al	al	PROPN
esrj-50337	90	41	.	.	PROPN
esrj-50337	90	42	,	,	PUNCT
esrj-50337	90	43	1996	1996	NUM
esrj-50337	90	44	;	;	PUNCT
esrj-50337	90	45	mirzaee	mirzaee	PROPN
esrj-50337	90	46	,	,	PUNCT
esrj-50337	90	47	2009	2009	NUM
esrj-50337	90	48	)	)	PUNCT
esrj-50337	90	49	.	.	PUNCT
esrj-50337	91	1	(	(	PUNCT
esrj-50337	91	2	14	14	NUM
esrj-50337	91	3	)	)	PUNCT
esrj-50337	91	4	one	one	NUM
esrj-50337	91	5	thousand	thousand	NUM
esrj-50337	91	6	data	datum	NOUN
esrj-50337	91	7	points	point	NOUN
esrj-50337	91	8	of	of	ADP
esrj-50337	91	9	the	the	DET
esrj-50337	91	10	above	above	ADJ
esrj-50337	91	11	format	format	NOUN
esrj-50337	91	12	were	be	AUX
esrj-50337	91	13	collected	collect	VERB
esrj-50337	91	14	.	.	PUNCT
esrj-50337	92	1	the	the	DET
esrj-50337	92	2	first	first	ADJ
esrj-50337	92	3	500	500	NUM
esrj-50337	92	4	were	be	AUX
esrj-50337	92	5	used	use	VERB
esrj-50337	92	6	for	for	ADP
esrj-50337	92	7	training	training	NOUN
esrj-50337	92	8	while	while	SCONJ
esrj-50337	92	9	the	the	DET
esrj-50337	92	10	others	other	NOUN
esrj-50337	92	11	were	be	AUX
esrj-50337	92	12	used	use	VERB
esrj-50337	92	13	for	for	ADP
esrj-50337	92	14	testing	test	VERB
esrj-50337	92	15	the	the	DET
esrj-50337	92	16	ann+pso	ann+pso	PROPN
esrj-50337	92	17	method	method	NOUN
esrj-50337	92	18	.	.	PUNCT
esrj-50337	93	1	then	then	ADV
esrj-50337	93	2	,	,	PUNCT
esrj-50337	93	3	the	the	DET
esrj-50337	93	4	following	follow	VERB
esrj-50337	93	5	case	case	NOUN
esrj-50337	93	6	was	be	AUX
esrj-50337	93	7	simulated	simulate	VERB
esrj-50337	93	8	with	with	ADP
esrj-50337	93	9	α	α	NOUN
esrj-50337	93	10	=	=	SYM
esrj-50337	93	11	0.2	0.2	NUM
esrj-50337	93	12	,	,	PUNCT
esrj-50337	93	13	β	β	X
esrj-50337	93	14	=	=	SYM
esrj-50337	93	15	0.1	0.1	NUM
esrj-50337	93	16	,	,	PUNCT
esrj-50337	93	17	x(0	x(0	PROPN
esrj-50337	93	18	)	)	PUNCT
esrj-50337	93	19	=	=	PROPN
esrj-50337	93	20	1.2	1.2	NUM
esrj-50337	93	21	and	and	CCONJ
esrj-50337	93	22	τ	τ	PROPN
esrj-50337	94	1	=	=	SYM
esrj-50337	94	2	17	17	NUM
esrj-50337	94	3	.	.	PUNCT
esrj-50337	95	1	from	from	ADP
esrj-50337	95	2	this	this	DET
esrj-50337	95	3	case	case	NOUN
esrj-50337	95	4	,	,	PUNCT
esrj-50337	95	5	several	several	ADJ
esrj-50337	95	6	network	network	NOUN
esrj-50337	95	7	architectures	architecture	NOUN
esrj-50337	95	8	were	be	AUX
esrj-50337	95	9	tested	test	VERB
esrj-50337	95	10	.	.	PUNCT
esrj-50337	96	1	the	the	DET
esrj-50337	96	2	most	most	ADV
esrj-50337	96	3	basic	basic	ADJ
esrj-50337	96	4	architecture	architecture	NOUN
esrj-50337	96	5	normally	normally	ADV
esrj-50337	96	6	used	use	VERB
esrj-50337	96	7	for	for	ADP
esrj-50337	96	8	the	the	DET
esrj-50337	96	9	analysis	analysis	NOUN
esrj-50337	96	10	of	of	ADP
esrj-50337	96	11	chaotic	chaotic	ADJ
esrj-50337	96	12	time	time	NOUN
esrj-50337	96	13	series	series	PROPN
esrj-50337	96	14	involves	involve	VERB
esrj-50337	96	15	a	a	DET
esrj-50337	96	16	neural	neural	ADJ
esrj-50337	96	17	network	network	NOUN
esrj-50337	96	18	consisting	consist	VERB
esrj-50337	96	19	of	of	ADP
esrj-50337	96	20	three	three	NUM
esrj-50337	96	21	or	or	CCONJ
esrj-50337	96	22	four	four	NUM
esrj-50337	96	23	layers	layer	NOUN
esrj-50337	96	24	(	(	PUNCT
esrj-50337	96	25	lazzús	lazzús	PROPN
esrj-50337	96	26	et	et	PROPN
esrj-50337	96	27	al	al	PROPN
esrj-50337	96	28	.	.	PROPN
esrj-50337	96	29	,	,	PUNCT
esrj-50337	96	30	2014	2014	NUM
esrj-50337	96	31	)	)	PUNCT
esrj-50337	96	32	.	.	PUNCT
esrj-50337	97	1	the	the	DET
esrj-50337	97	2	input	input	NOUN
esrj-50337	97	3	layer	layer	NOUN
esrj-50337	97	4	contains	contain	VERB
esrj-50337	97	5	one	one	NUM
esrj-50337	97	6	neuron	neuron	NOUN
esrj-50337	97	7	for	for	ADP
esrj-50337	97	8	each	each	DET
esrj-50337	97	9	input	input	NOUN
esrj-50337	97	10	parameter	parameter	NOUN
esrj-50337	97	11	:	:	PUNCT
esrj-50337	97	12	x(t	x(t	PROPN
esrj-50337	97	13	)	)	PUNCT
esrj-50337	97	14	,	,	PUNCT
esrj-50337	97	15	x(t–6	x(t–6	PROPN
esrj-50337	97	16	)	)	PUNCT
esrj-50337	97	17	,	,	PUNCT
esrj-50337	97	18	x(t–12	x(t–12	NUM
esrj-50337	97	19	)	)	PUNCT
esrj-50337	97	20	,	,	PUNCT
esrj-50337	97	21	and	and	CCONJ
esrj-50337	97	22	x(t–16	x(t–16	NUM
esrj-50337	97	23	)	)	PUNCT
esrj-50337	97	24	.	.	PUNCT
esrj-50337	98	1	the	the	DET
esrj-50337	98	2	output	output	NOUN
esrj-50337	98	3	layer	layer	NOUN
esrj-50337	98	4	has	have	VERB
esrj-50337	98	5	one	one	NUM
esrj-50337	98	6	node	node	NOUN
esrj-50337	98	7	generating	generate	VERB
esrj-50337	98	8	the	the	DET
esrj-50337	98	9	scaled	scale	VERB
esrj-50337	98	10	estimated	estimate	VERB
esrj-50337	98	11	value	value	NOUN
esrj-50337	98	12	of	of	ADP
esrj-50337	98	13	the	the	DET
esrj-50337	98	14	chaotic	chaotic	ADJ
esrj-50337	98	15	time	time	NOUN
esrj-50337	98	16	series	series	PROPN
esrj-50337	98	17	x(t+84	x(t+84	PROPN
esrj-50337	98	18	)	)	PUNCT
esrj-50337	98	19	.	.	PUNCT
esrj-50337	99	1	the	the	DET
esrj-50337	99	2	number	number	NOUN
esrj-50337	99	3	of	of	ADP
esrj-50337	99	4	hidden	hide	VERB
esrj-50337	99	5	neurons	neuron	NOUN
esrj-50337	99	6	needs	need	VERB
esrj-50337	99	7	to	to	PART
esrj-50337	99	8	be	be	AUX
esrj-50337	99	9	sufficient	sufficient	ADJ
esrj-50337	99	10	to	to	PART
esrj-50337	99	11	ensure	ensure	VERB
esrj-50337	99	12	that	that	SCONJ
esrj-50337	99	13	the	the	DET
esrj-50337	99	14	information	information	NOUN
esrj-50337	99	15	contained	contain	VERB
esrj-50337	99	16	in	in	ADP
esrj-50337	99	17	the	the	DET
esrj-50337	99	18	data	datum	NOUN
esrj-50337	99	19	utilized	utilize	VERB
esrj-50337	99	20	for	for	ADP
esrj-50337	99	21	training	train	VERB
esrj-50337	99	22	the	the	DET
esrj-50337	99	23	network	network	NOUN
esrj-50337	99	24	is	be	AUX
esrj-50337	99	25	adequately	adequately	ADV
esrj-50337	99	26	represented	represent	VERB
esrj-50337	99	27	(	(	PUNCT
esrj-50337	99	28	lazzús	lazzús	PROPN
esrj-50337	99	29	et	et	PROPN
esrj-50337	99	30	al	al	PROPN
esrj-50337	99	31	.	.	PROPN
esrj-50337	99	32	,	,	PUNCT
esrj-50337	99	33	2014	2014	NUM
esrj-50337	99	34	)	)	PUNCT
esrj-50337	99	35	.	.	PUNCT
esrj-50337	100	1	there	there	PRON
esrj-50337	100	2	is	be	VERB
esrj-50337	100	3	no	no	DET
esrj-50337	100	4	specific	specific	ADJ
esrj-50337	100	5	approach	approach	NOUN
esrj-50337	100	6	to	to	PART
esrj-50337	100	7	determine	determine	VERB
esrj-50337	100	8	the	the	DET
esrj-50337	100	9	number	number	NOUN
esrj-50337	100	10	of	of	ADP
esrj-50337	100	11	neurons	neuron	NOUN
esrj-50337	100	12	of	of	ADP
esrj-50337	100	13	the	the	DET
esrj-50337	100	14	hidden	hide	VERB
esrj-50337	100	15	layer	layer	NOUN
esrj-50337	100	16	,	,	PUNCT
esrj-50337	100	17	many	many	ADJ
esrj-50337	100	18	alternative	alternative	ADJ
esrj-50337	100	19	combinations	combination	NOUN
esrj-50337	100	20	are	be	AUX
esrj-50337	100	21	possible	possible	ADJ
esrj-50337	100	22	.	.	PUNCT
esrj-50337	101	1	the	the	DET
esrj-50337	101	2	optimum	optimum	ADJ
esrj-50337	101	3	number	number	NOUN
esrj-50337	101	4	of	of	ADP
esrj-50337	101	5	neurons	neuron	NOUN
esrj-50337	101	6	was	be	AUX
esrj-50337	101	7	determined	determine	VERB
esrj-50337	101	8	by	by	ADP
esrj-50337	101	9	adding	add	VERB
esrj-50337	101	10	neurons	neuron	NOUN
esrj-50337	101	11	in	in	ADP
esrj-50337	101	12	systematic	systematic	ADJ
esrj-50337	101	13	form	form	NOUN
esrj-50337	101	14	and	and	CCONJ
esrj-50337	101	15	evaluating	evaluate	VERB
esrj-50337	101	16	the	the	DET
esrj-50337	101	17	mse	mse	NOUN
esrj-50337	101	18	and	and	CCONJ
esrj-50337	101	19	rmse	rmse	NOUN
esrj-50337	101	20	of	of	ADP
esrj-50337	101	21	the	the	DET
esrj-50337	101	22	sets	set	NOUN
esrj-50337	101	23	during	during	ADP
esrj-50337	101	24	the	the	DET
esrj-50337	101	25	learning	learning	NOUN
esrj-50337	101	26	process	process	NOUN
esrj-50337	101	27	(	(	PUNCT
esrj-50337	101	28	pérez	pérez	NOUN
esrj-50337	101	29	ponce	ponce	PROPN
esrj-50337	101	30	et	et	PROPN
esrj-50337	101	31	al	al	PROPN
esrj-50337	101	32	.	.	PROPN
esrj-50337	101	33	,	,	PUNCT
esrj-50337	101	34	2012	2012	NUM
esrj-50337	101	35	;	;	PUNCT
esrj-50337	101	36	lazzús	lazzús	PROPN
esrj-50337	101	37	et	et	PROPN
esrj-50337	101	38	al	al	PROPN
esrj-50337	101	39	.	.	PROPN
esrj-50337	101	40	,	,	PUNCT
esrj-50337	101	41	2014	2014	NUM
esrj-50337	101	42	)	)	PUNCT
esrj-50337	101	43	.	.	PUNCT
esrj-50337	102	1	for	for	ADP
esrj-50337	102	2	our	our	PRON
esrj-50337	102	3	case	case	NOUN
esrj-50337	102	4	the	the	DET
esrj-50337	102	5	optimum	optimum	ADJ
esrj-50337	102	6	architecture	architecture	NOUN
esrj-50337	102	7	was	be	AUX
esrj-50337	102	8	4	4	NUM
esrj-50337	102	9	-	-	SYM
esrj-50337	102	10	12	12	NUM
esrj-50337	102	11	-	-	SYM
esrj-50337	102	12	1	1	NUM
esrj-50337	102	13	.	.	PUNCT
esrj-50337	103	1	the	the	DET
esrj-50337	103	2	results	result	NOUN
esrj-50337	103	3	obtained	obtain	VERB
esrj-50337	103	4	with	with	ADP
esrj-50337	103	5	the	the	DET
esrj-50337	103	6	ann+pso	ann+pso	PROPN
esrj-50337	103	7	method	method	NOUN
esrj-50337	103	8	present	present	VERB
esrj-50337	103	9	a	a	DET
esrj-50337	103	10	mse=0.000063	mse=0.000063	ADJ
esrj-50337	103	11	and	and	CCONJ
esrj-50337	103	12	rmse=0.0079	rmse=0.0079	PROPN
esrj-50337	103	13	for	for	ADP
esrj-50337	103	14	training	training	NOUN
esrj-50337	103	15	set	set	NOUN
esrj-50337	103	16	,	,	PUNCT
esrj-50337	103	17	and	and	CCONJ
esrj-50337	103	18	mse=0.000065	mse=0.000065	NOUN
esrj-50337	103	19	and	and	CCONJ
esrj-50337	103	20	rmse=0.0080	rmse=0.0080	NOUN
esrj-50337	103	21	for	for	ADP
esrj-50337	103	22	prediction	prediction	NOUN
esrj-50337	103	23	set	set	NOUN
esrj-50337	103	24	.	.	PUNCT
esrj-50337	104	1	these	these	DET
esrj-50337	104	2	results	result	NOUN
esrj-50337	104	3	show	show	VERB
esrj-50337	104	4	that	that	SCONJ
esrj-50337	104	5	the	the	DET
esrj-50337	104	6	ann+pso	ann+pso	PROPN
esrj-50337	104	7	model	model	NOUN
esrj-50337	104	8	can	can	AUX
esrj-50337	104	9	be	be	AUX
esrj-50337	104	10	accurately	accurately	ADV
esrj-50337	104	11	trained	train	VERB
esrj-50337	104	12	and	and	CCONJ
esrj-50337	104	13	that	that	SCONJ
esrj-50337	104	14	the	the	DET
esrj-50337	104	15	chosen	choose	VERB
esrj-50337	104	16	architectures	architecture	NOUN
esrj-50337	104	17	can	can	AUX
esrj-50337	104	18	predict	predict	VERB
esrj-50337	104	19	the	the	DET
esrj-50337	104	20	long	long	ADJ
esrj-50337	104	21	-	-	PUNCT
esrj-50337	104	22	term	term	NOUN
esrj-50337	104	23	x(t+84	x(t+84	PROPN
esrj-50337	104	24	)	)	PUNCT
esrj-50337	104	25	with	with	ADP
esrj-50337	104	26	acceptable	acceptable	ADJ
esrj-50337	104	27	accuracy	accuracy	NOUN
esrj-50337	104	28	.	.	PUNCT
esrj-50337	105	1	table	table	NOUN
esrj-50337	105	2	2	2	NUM
esrj-50337	105	3	shows	show	VERB
esrj-50337	105	4	a	a	DET
esrj-50337	105	5	comparison	comparison	NOUN
esrj-50337	105	6	between	between	ADP
esrj-50337	105	7	some	some	DET
esrj-50337	105	8	computational	computational	ADJ
esrj-50337	105	9	methods	method	NOUN
esrj-50337	105	10	found	find	VERB
esrj-50337	105	11	in	in	ADP
esrj-50337	105	12	the	the	DET
esrj-50337	105	13	literature	literature	NOUN
esrj-50337	105	14	(	(	PUNCT
esrj-50337	105	15	martinetz	martinetz	VERB
esrj-50337	105	16	et	et	PROPN
esrj-50337	105	17	al	al	PROPN
esrj-50337	105	18	.	.	PROPN
esrj-50337	105	19	,	,	PUNCT
esrj-50337	105	20	1993	1993	NUM
esrj-50337	105	21	;	;	PUNCT
esrj-50337	105	22	whitehead	whitehead	PROPN
esrj-50337	105	23	and	and	CCONJ
esrj-50337	105	24	choate	choate	PROPN
esrj-50337	105	25	,	,	PUNCT
esrj-50337	105	26	1996	1996	NUM
esrj-50337	105	27	;	;	PUNCT
esrj-50337	106	1	bersini	bersini	PROPN
esrj-50337	106	2	et	et	PROPN
esrj-50337	106	3	al	al	PROPN
esrj-50337	106	4	.	.	PROPN
esrj-50337	106	5	,	,	PUNCT
esrj-50337	106	6	1997	1997	NUM
esrj-50337	106	7	;	;	PUNCT
esrj-50337	106	8	awad	awad	PROPN
esrj-50337	106	9	et	et	PROPN
esrj-50337	106	10	al	al	PROPN
esrj-50337	106	11	.	.	PROPN
esrj-50337	106	12	,	,	PUNCT
esrj-50337	106	13	2009	2009	NUM
esrj-50337	106	14	)	)	PUNCT
esrj-50337	106	15	and	and	CCONJ
esrj-50337	106	16	the	the	DET
esrj-50337	106	17	result	result	NOUN
esrj-50337	106	18	obtained	obtain	VERB
esrj-50337	106	19	with	with	ADP
esrj-50337	106	20	the	the	DET
esrj-50337	106	21	ann+pso	ann+pso	PROPN
esrj-50337	106	22	method	method	NOUN
esrj-50337	106	23	.	.	PUNCT
esrj-50337	107	1	this	this	DET
esrj-50337	107	2	comparison	comparison	NOUN
esrj-50337	107	3	was	be	AUX
esrj-50337	107	4	made	make	VERB
esrj-50337	107	5	using	use	VERB
esrj-50337	107	6	the	the	DET
esrj-50337	107	7	normalized	normalize	VERB
esrj-50337	107	8	root	root	NOUN
esrj-50337	107	9	mean	mean	VERB
esrj-50337	107	10	squared	square	VERB
esrj-50337	107	11	error	error	NOUN
esrj-50337	107	12	(	(	PUNCT
esrj-50337	107	13	nrmse	nrmse	PROPN
esrj-50337	107	14	)	)	PUNCT
esrj-50337	107	15	,	,	PUNCT
esrj-50337	107	16	defined	define	VERB
esrj-50337	107	17	as	as	ADP
esrj-50337	107	18	:	:	PUNCT
esrj-50337	107	19	(	(	PUNCT
esrj-50337	107	20	15	15	NUM
esrj-50337	107	21	)	)	PUNCT
esrj-50337	107	22	table	table	NOUN
esrj-50337	107	23	2	2	NUM
esrj-50337	107	24	.	.	PUNCT
esrj-50337	107	25	comparison	comparison	NOUN
esrj-50337	107	26	between	between	ADP
esrj-50337	107	27	computational	computational	ADJ
esrj-50337	107	28	methods	method	NOUN
esrj-50337	107	29	found	find	VERB
esrj-50337	107	30	in	in	ADP
esrj-50337	107	31	the	the	DET
esrj-50337	107	32	literature	literature	NOUN
esrj-50337	107	33	for	for	ADP
esrj-50337	107	34	the	the	DET
esrj-50337	107	35	long	long	ADJ
esrj-50337	107	36	-	-	PUNCT
esrj-50337	107	37	term	term	NOUN
esrj-50337	107	38	prediction	prediction	NOUN
esrj-50337	107	39	.	.	PUNCT
esrj-50337	108	1	3.2	3.2	NUM
esrj-50337	108	2	.	.	PUNCT
esrj-50337	109	1	wind	wind	NOUN
esrj-50337	109	2	speed	speed	NOUN
esrj-50337	109	3	time	time	NOUN
esrj-50337	109	4	series	series	NOUN
esrj-50337	109	5	once	once	SCONJ
esrj-50337	109	6	the	the	DET
esrj-50337	109	7	capability	capability	NOUN
esrj-50337	109	8	of	of	ADP
esrj-50337	109	9	the	the	DET
esrj-50337	109	10	hybrid	hybrid	ADJ
esrj-50337	109	11	algorithm	algorithm	NOUN
esrj-50337	109	12	was	be	AUX
esrj-50337	109	13	proved	prove	VERB
esrj-50337	109	14	,	,	PUNCT
esrj-50337	109	15	it	it	PRON
esrj-50337	109	16	was	be	AUX
esrj-50337	109	17	used	use	VERB
esrj-50337	109	18	to	to	PART
esrj-50337	109	19	forecast	forecast	VERB
esrj-50337	109	20	the	the	DET
esrj-50337	109	21	long	long	ADJ
esrj-50337	109	22	-	-	PUNCT
esrj-50337	109	23	term	term	NOUN
esrj-50337	109	24	of	of	ADP
esrj-50337	109	25	wind	wind	NOUN
esrj-50337	109	26	speed	speed	NOUN
esrj-50337	109	27	time	time	NOUN
esrj-50337	109	28	series	series	NOUN
esrj-50337	109	29	.	.	PUNCT
esrj-50337	110	1	this	this	DET
esrj-50337	110	2	study	study	NOUN
esrj-50337	110	3	is	be	AUX
esrj-50337	110	4	based	base	VERB
esrj-50337	110	5	in	in	ADP
esrj-50337	110	6	data	datum	NOUN
esrj-50337	110	7	collected	collect	VERB
esrj-50337	110	8	from	from	ADP
esrj-50337	110	9	a	a	DET
esrj-50337	110	10	meteorological	meteorological	ADJ
esrj-50337	110	11	stations	station	NOUN
esrj-50337	110	12	located	locate	VERB
esrj-50337	110	13	on	on	ADP
esrj-50337	110	14	the	the	DET
esrj-50337	110	15	semi	semi	ADJ
esrj-50337	110	16	-	-	ADJ
esrj-50337	110	17	arid	arid	ADJ
esrj-50337	110	18	norte	norte	PROPN
esrj-50337	110	19	chico	chico	PROPN
esrj-50337	110	20	of	of	ADP
esrj-50337	110	21	chile	chile	PROPN
esrj-50337	110	22	(	(	PUNCT
esrj-50337	110	23	29º54	29º54	NUM
esrj-50337	110	24	’	'	PUNCT
esrj-50337	110	25	s	s	PART
esrj-50337	110	26	;	;	PUNCT
esrj-50337	110	27	71º15	71º15	NUM
esrj-50337	110	28	’	'	PUNCT
esrj-50337	110	29	w	w	NOUN
esrj-50337	110	30	;	;	PUNCT
esrj-50337	110	31	10	10	NUM
esrj-50337	110	32	m	m	NOUN
esrj-50337	110	33	)	)	PUNCT
esrj-50337	110	34	,	,	PUNCT
esrj-50337	110	35	located	locate	VERB
esrj-50337	110	36	at	at	ADP
esrj-50337	110	37	south	south	NOUN
esrj-50337	110	38	of	of	ADP
esrj-50337	110	39	the	the	DET
esrj-50337	110	40	hyper	hyper	ADJ
esrj-50337	110	41	-	-	ADJ
esrj-50337	110	42	arid	arid	ADJ
esrj-50337	110	43	atacama	atacama	PROPN
esrj-50337	110	44	desert	desert	PROPN
esrj-50337	110	45	.	.	PUNCT
esrj-50337	111	1	the	the	DET
esrj-50337	111	2	region	region	NOUN
esrj-50337	111	3	is	be	AUX
esrj-50337	111	4	characterized	characterize	VERB
esrj-50337	111	5	by	by	ADP
esrj-50337	111	6	complex	complex	ADJ
esrj-50337	111	7	topography	topography	NOUN
esrj-50337	111	8	with	with	ADP
esrj-50337	111	9	altitudes	altitude	NOUN
esrj-50337	111	10	varying	vary	VERB
esrj-50337	111	11	from	from	ADP
esrj-50337	111	12	sea	sea	NOUN
esrj-50337	111	13	level	level	NOUN
esrj-50337	111	14	until	until	ADP
esrj-50337	111	15	5000	5000	NUM
esrj-50337	111	16	m	m	VERB
esrj-50337	111	17	at	at	ADP
esrj-50337	111	18	the	the	DET
esrj-50337	111	19	high	high	ADJ
esrj-50337	111	20	andes	andes	PROPN
esrj-50337	111	21	cordillera	cordillera	PROPN
esrj-50337	111	22	.	.	PUNCT
esrj-50337	112	1	the	the	DET
esrj-50337	112	2	climatic	climatic	ADJ
esrj-50337	112	3	characteristics	characteristic	NOUN
esrj-50337	112	4	are	be	AUX
esrj-50337	112	5	influenced	influence	VERB
esrj-50337	112	6	by	by	ADP
esrj-50337	112	7	the	the	DET
esrj-50337	112	8	south	south	ADJ
esrj-50337	112	9	-	-	PUNCT
esrj-50337	112	10	eastern	eastern	ADJ
esrj-50337	112	11	pacific	pacific	ADJ
esrj-50337	112	12	subtropical	subtropical	ADJ
esrj-50337	112	13	anticyclone	anticyclone	NOUN
esrj-50337	112	14	and	and	CCONJ
esrj-50337	112	15	the	the	DET
esrj-50337	112	16	cold	cold	PROPN
esrj-50337	112	17	humbolt	humbolt	PROPN
esrj-50337	112	18	current	current	PROPN
esrj-50337	112	19	which	which	PRON
esrj-50337	112	20	results	result	VERB
esrj-50337	112	21	in	in	ADP
esrj-50337	112	22	low	low	ADJ
esrj-50337	112	23	precipitation	precipitation	NOUN
esrj-50337	112	24	rates	rate	NOUN
esrj-50337	112	25	(	(	PUNCT
esrj-50337	112	26	kalthoff	kalthoff	NOUN
esrj-50337	112	27	et	et	PROPN
esrj-50337	112	28	al	al	PROPN
esrj-50337	112	29	.	.	PROPN
esrj-50337	112	30	,	,	PUNCT
esrj-50337	112	31	2002	2002	NUM
esrj-50337	112	32	)	)	PUNCT
esrj-50337	112	33	.	.	PUNCT
esrj-50337	113	1	the	the	DET
esrj-50337	113	2	zone	zone	NOUN
esrj-50337	113	3	is	be	AUX
esrj-50337	113	4	one	one	NUM
esrj-50337	113	5	of	of	ADP
esrj-50337	113	6	the	the	DET
esrj-50337	113	7	most	most	ADV
esrj-50337	113	8	sensitive	sensitive	ADJ
esrj-50337	113	9	areas	area	NOUN
esrj-50337	113	10	in	in	ADP
esrj-50337	113	11	south	south	PROPN
esrj-50337	113	12	america	america	PROPN
esrj-50337	113	13	(	(	PUNCT
esrj-50337	113	14	kalthoff	kalthoff	NOUN
esrj-50337	113	15	et	et	PROPN
esrj-50337	113	16	al	al	PROPN
esrj-50337	113	17	.	.	PROPN
esrj-50337	113	18	,	,	PUNCT
esrj-50337	113	19	2006	2006	NUM
esrj-50337	113	20	)	)	PUNCT
esrj-50337	113	21	,	,	PUNCT
esrj-50337	113	22	and	and	CCONJ
esrj-50337	113	23	recent	recent	ADJ
esrj-50337	113	24	studies	study	NOUN
esrj-50337	113	25	of	of	ADP
esrj-50337	113	26	oceanic	oceanic	ADJ
esrj-50337	113	27	and	and	CCONJ
esrj-50337	113	28	atmospheric	atmospheric	ADJ
esrj-50337	113	29	variability	variability	NOUN
esrj-50337	113	30	have	have	AUX
esrj-50337	113	31	confirmed	confirm	VERB
esrj-50337	113	32	the	the	DET
esrj-50337	113	33	implications	implication	NOUN
esrj-50337	113	34	of	of	ADP
esrj-50337	113	35	the	the	DET
esrj-50337	113	36	dynamics	dynamic	NOUN
esrj-50337	113	37	of	of	ADP
esrj-50337	113	38	el	el	PROPN
esrj-50337	113	39	niño	niño	PROPN
esrj-50337	113	40	-	-	PUNCT
esrj-50337	113	41	southern	southern	ADJ
esrj-50337	113	42	oscillation	oscillation	NOUN
esrj-50337	113	43	(	(	PUNCT
esrj-50337	113	44	enso	enso	PROPN
esrj-50337	113	45	)	)	PUNCT
esrj-50337	113	46	cycle	cycle	NOUN
esrj-50337	113	47	on	on	ADP
esrj-50337	113	48	the	the	DET
esrj-50337	113	49	local	local	ADJ
esrj-50337	113	50	climate	climate	NOUN
esrj-50337	113	51	of	of	ADP
esrj-50337	113	52	this	this	DET
esrj-50337	113	53	zone	zone	NOUN
esrj-50337	113	54	(	(	PUNCT
esrj-50337	113	55	meinen	meinen	NOUN
esrj-50337	113	56	and	and	CCONJ
esrj-50337	113	57	mcphaden	mcphaden	ADJ
esrj-50337	113	58	,	,	PUNCT
esrj-50337	113	59	2009	2009	NUM
esrj-50337	113	60	)	)	PUNCT
esrj-50337	113	61	.	.	PUNCT
esrj-50337	114	1	43800	43800	NUM
esrj-50337	114	2	data	datum	NOUN
esrj-50337	114	3	points	point	NOUN
esrj-50337	114	4	(	(	PUNCT
esrj-50337	114	5	years	year	NOUN
esrj-50337	114	6	2003	2003	NUM
esrj-50337	114	7	-	-	SYM
esrj-50337	114	8	2007	2007	NUM
esrj-50337	114	9	)	)	PUNCT
esrj-50337	114	10	were	be	AUX
esrj-50337	114	11	used	use	VERB
esrj-50337	114	12	.	.	PUNCT
esrj-50337	115	1	the	the	DET
esrj-50337	115	2	future	future	ADJ
esrj-50337	115	3	values	value	NOUN
esrj-50337	115	4	of	of	ADP
esrj-50337	115	5	wind	wind	NOUN
esrj-50337	115	6	speed	speed	NOUN
esrj-50337	115	7	was	be	AUX
esrj-50337	115	8	predicted	predict	VERB
esrj-50337	115	9	using	use	VERB
esrj-50337	115	10	the	the	DET
esrj-50337	115	11	past	past	ADJ
esrj-50337	115	12	values	value	NOUN
esrj-50337	115	13	of	of	ADP
esrj-50337	115	14	the	the	DET
esrj-50337	115	15	time	time	NOUN
esrj-50337	115	16	series	series	NOUN
esrj-50337	115	17	of	of	ADP
esrj-50337	115	18	wind	wind	NOUN
esrj-50337	115	19	speed	speed	NOUN
esrj-50337	115	20	ws(m·s−1	ws(m·s−1	NOUN
esrj-50337	115	21	)	)	PUNCT
esrj-50337	115	22	,	,	PUNCT
esrj-50337	115	23	relative	relative	ADJ
esrj-50337	115	24	humidity	humidity	NOUN
esrj-50337	115	25	rh(%	rh(%	NOUN
esrj-50337	115	26	)	)	PUNCT
esrj-50337	115	27	,	,	PUNCT
esrj-50337	115	28	and	and	CCONJ
esrj-50337	115	29	air	air	NOUN
esrj-50337	115	30	temperature	temperature	NOUN
esrj-50337	115	31	t(k	t(k	PROPN
esrj-50337	115	32	)	)	PUNCT
esrj-50337	115	33	.	.	PUNCT
esrj-50337	116	1	figure	figure	NOUN
esrj-50337	116	2	2	2	NUM
esrj-50337	116	3	shows	show	VERB
esrj-50337	116	4	the	the	DET
esrj-50337	116	5	time	time	NOUN
esrj-50337	116	6	series	series	NOUN
esrj-50337	116	7	of	of	ADP
esrj-50337	116	8	the	the	DET
esrj-50337	116	9	selected	select	VERB
esrj-50337	116	10	meteorological	meteorological	ADJ
esrj-50337	116	11	data	datum	NOUN
esrj-50337	116	12	used	use	VERB
esrj-50337	116	13	.	.	PUNCT
esrj-50337	117	1	the	the	DET
esrj-50337	117	2	data	datum	NOUN
esrj-50337	117	3	ranges	range	VERB
esrj-50337	117	4	and	and	CCONJ
esrj-50337	117	5	the	the	DET
esrj-50337	117	6	properties	property	NOUN
esrj-50337	117	7	of	of	ADP
esrj-50337	117	8	interest	interest	NOUN
esrj-50337	117	9	are	be	AUX
esrj-50337	117	10	listed	list	VERB
esrj-50337	117	11	in	in	ADP
esrj-50337	117	12	table	table	NOUN
esrj-50337	117	13	3	3	NUM
esrj-50337	117	14	.	.	PUNCT
esrj-50337	118	1	as	as	SCONJ
esrj-50337	118	2	seen	see	VERB
esrj-50337	118	3	in	in	ADP
esrj-50337	118	4	this	this	DET
esrj-50337	118	5	table	table	NOUN
esrj-50337	118	6	,	,	PUNCT
esrj-50337	118	7	hourly	hourly	ADJ
esrj-50337	118	8	ws	ws	NOUN
esrj-50337	118	9	cover	cover	VERB
esrj-50337	118	10	wide	wide	ADJ
esrj-50337	118	11	ranges	range	NOUN
esrj-50337	118	12	,	,	PUNCT
esrj-50337	118	13	going	go	VERB
esrj-50337	118	14	from	from	ADP
esrj-50337	118	15	≈0	≈0	PROPN
esrj-50337	118	16	to	to	ADP
esrj-50337	118	17	10	10	NUM
esrj-50337	118	18	(	(	PUNCT
esrj-50337	118	19	m·s−1	m·s−1	NOUN
esrj-50337	118	20	)	)	PUNCT
esrj-50337	118	21	.	.	PUNCT
esrj-50337	119	1	other	other	ADJ
esrj-50337	119	2	wide	wide	ADJ
esrj-50337	119	3	range	range	NOUN
esrj-50337	119	4	for	for	ADP
esrj-50337	119	5	the	the	DET
esrj-50337	119	6	input	input	NOUN
esrj-50337	119	7	parameters	parameter	NOUN
esrj-50337	119	8	are	be	AUX
esrj-50337	119	9	:	:	PUNCT
esrj-50337	119	10	t	t	X
esrj-50337	119	11	from	from	ADP
esrj-50337	119	12	270	270	NUM
esrj-50337	119	13	to	to	ADP
esrj-50337	119	14	305	305	NUM
esrj-50337	119	15	(	(	PUNCT
esrj-50337	119	16	k	k	NOUN
esrj-50337	119	17	)	)	PUNCT
esrj-50337	119	18	,	,	PUNCT
esrj-50337	119	19	and	and	CCONJ
esrj-50337	119	20	rh	rh	NOUN
esrj-50337	119	21	from	from	ADP
esrj-50337	119	22	2	2	NUM
esrj-50337	119	23	to	to	ADP
esrj-50337	119	24	≈100	≈100	PROPN
esrj-50337	119	25	(	(	PUNCT
esrj-50337	119	26	%	%	NOUN
esrj-50337	119	27	)	)	PUNCT
esrj-50337	119	28	.	.	PUNCT
esrj-50337	120	1	the	the	DET
esrj-50337	120	2	influence	influence	NOUN
esrj-50337	120	3	of	of	ADP
esrj-50337	120	4	the	the	DET
esrj-50337	120	5	selected	select	VERB
esrj-50337	120	6	meteorological	meteorological	ADJ
esrj-50337	120	7	data	datum	NOUN
esrj-50337	120	8	in	in	ADP
esrj-50337	120	9	this	this	DET
esrj-50337	120	10	study	study	NOUN
esrj-50337	120	11	(	(	PUNCT
esrj-50337	120	12	t	t	PROPN
esrj-50337	120	13	,	,	PUNCT
esrj-50337	120	14	rh	rh	PROPN
esrj-50337	120	15	,	,	PUNCT
esrj-50337	120	16	and	and	CCONJ
esrj-50337	120	17	ws	ws	NOUN
esrj-50337	120	18	)	)	PUNCT
esrj-50337	120	19	over	over	ADP
esrj-50337	120	20	the	the	DET
esrj-50337	120	21	climate	climate	NOUN
esrj-50337	120	22	of	of	ADP
esrj-50337	120	23	the	the	DET
esrj-50337	120	24	semi	semi	ADJ
esrj-50337	120	25	-	-	ADJ
esrj-50337	120	26	arid	arid	ADJ
esrj-50337	120	27	zone	zone	NOUN
esrj-50337	120	28	of	of	ADP
esrj-50337	120	29	the	the	DET
esrj-50337	120	30	atacama	atacama	PROPN
esrj-50337	120	31	desert	desert	NOUN
esrj-50337	120	32	has	have	AUX
esrj-50337	120	33	been	be	AUX
esrj-50337	120	34	revised	revise	VERB
esrj-50337	120	35	and	and	CCONJ
esrj-50337	120	36	evaluated	evaluate	VERB
esrj-50337	120	37	in	in	ADP
esrj-50337	120	38	other	other	ADJ
esrj-50337	120	39	communications	communication	NOUN
esrj-50337	120	40	(	(	PUNCT
esrj-50337	120	41	kalthoff	kalthoff	NOUN
esrj-50337	120	42	et	et	PROPN
esrj-50337	120	43	al	al	PROPN
esrj-50337	120	44	.	.	PROPN
esrj-50337	120	45	,	,	PUNCT
esrj-50337	120	46	2002	2002	NUM
esrj-50337	120	47	;	;	PUNCT
esrj-50337	120	48	kalthoff	kalthoff	NOUN
esrj-50337	120	49	et	et	PROPN
esrj-50337	120	50	al	al	PROPN
esrj-50337	120	51	.	.	PROPN
esrj-50337	120	52	,	,	PUNCT
esrj-50337	120	53	2006	2006	NUM
esrj-50337	120	54	)	)	PUNCT
esrj-50337	120	55	.	.	PUNCT
esrj-50337	121	1	figure	figure	NOUN
esrj-50337	121	2	3	3	NUM
esrj-50337	121	3	shows	show	VERB
esrj-50337	121	4	ws	ws	NOUN
esrj-50337	121	5	as	as	ADP
esrj-50337	121	6	a	a	DET
esrj-50337	121	7	function	function	NOUN
esrj-50337	121	8	of	of	ADP
esrj-50337	121	9	the	the	DET
esrj-50337	121	10	selected	select	VERB
esrj-50337	121	11	meteorological	meteorological	ADJ
esrj-50337	121	12	data	datum	NOUN
esrj-50337	121	13	:	:	PUNCT
esrj-50337	121	14	t	t	PROPN
esrj-50337	121	15	and	and	CCONJ
esrj-50337	121	16	rh	rh	PROPN
esrj-50337	121	17	.	.	PROPN
esrj-50337	121	18	fig	fig	PROPN
esrj-50337	121	19	.	.	PUNCT
esrj-50337	122	1	3a	3a	PROPN
esrj-50337	122	2	shows	show	VERB
esrj-50337	122	3	ws	ws	PROPN
esrj-50337	122	4	as	as	ADP
esrj-50337	122	5	a	a	DET
esrj-50337	122	6	function	function	NOUN
esrj-50337	122	7	of	of	ADP
esrj-50337	122	8	t	t	PROPN
esrj-50337	122	9	with	with	ADP
esrj-50337	122	10	a	a	DET
esrj-50337	122	11	coefficient	coefficient	NOUN
esrj-50337	122	12	of	of	ADP
esrj-50337	122	13	linear	linear	ADJ
esrj-50337	122	14	correlation	correlation	NOUN
esrj-50337	122	15	(	(	PUNCT
esrj-50337	122	16	r2	r2	PROPN
esrj-50337	122	17	)	)	PUNCT
esrj-50337	122	18	of	of	ADP
esrj-50337	122	19	0.5979	0.5979	NUM
esrj-50337	122	20	.	.	PUNCT
esrj-50337	123	1	fig	fig	NOUN
esrj-50337	123	2	.	.	PUNCT
esrj-50337	124	1	3b	3b	PROPN
esrj-50337	124	2	shows	show	VERB
esrj-50337	124	3	ws	ws	NOUN
esrj-50337	124	4	as	as	ADP
esrj-50337	124	5	a	a	DET
esrj-50337	124	6	function	function	NOUN
esrj-50337	124	7	of	of	ADP
esrj-50337	124	8	rh	rh	PROPN
esrj-50337	124	9	with	with	ADP
esrj-50337	124	10	r2	r2	PROPN
esrj-50337	124	11	of	of	ADP
esrj-50337	124	12	0.2757	0.2757	NUM
esrj-50337	124	13	.	.	PUNCT
esrj-50337	125	1	note	note	VERB
esrj-50337	125	2	that	that	SCONJ
esrj-50337	125	3	the	the	DET
esrj-50337	125	4	coefficient	coefficient	NOUN
esrj-50337	125	5	of	of	ADP
esrj-50337	125	6	linear	linear	ADJ
esrj-50337	125	7	correlation	correlation	NOUN
esrj-50337	125	8	in	in	ADP
esrj-50337	125	9	this	this	DET
esrj-50337	125	10	figure	figure	NOUN
esrj-50337	125	11	shows	show	VERB
esrj-50337	125	12	a	a	DET
esrj-50337	125	13	non	non	ADJ
esrj-50337	125	14	-	-	ADJ
esrj-50337	125	15	linear	linear	ADJ
esrj-50337	125	16	relationship	relationship	NOUN
esrj-50337	125	17	between	between	ADP
esrj-50337	125	18	table	table	NOUN
esrj-50337	125	19	3	3	NUM
esrj-50337	125	20	summary	summary	NOUN
esrj-50337	125	21	of	of	ADP
esrj-50337	125	22	data	datum	NOUN
esrj-50337	125	23	used	use	VERB
esrj-50337	125	24	in	in	ADP
esrj-50337	125	25	this	this	DET
esrj-50337	125	26	study	study	NOUN
esrj-50337	125	27	.	.	PUNCT
esrj-50337	126	1	figure	figure	NOUN
esrj-50337	126	2	2	2	NUM
esrj-50337	126	3	.	.	NOUN
esrj-50337	126	4	time	time	NOUN
esrj-50337	126	5	series	series	NOUN
esrj-50337	126	6	of	of	ADP
esrj-50337	126	7	the	the	DET
esrj-50337	126	8	meteorological	meteorological	ADJ
esrj-50337	126	9	data	datum	NOUN
esrj-50337	126	10	used	use	VERB
esrj-50337	126	11	in	in	ADP
esrj-50337	126	12	this	this	DET
esrj-50337	126	13	study	study	NOUN
esrj-50337	126	14	.	.	PUNCT
esrj-50337	127	1	(	(	PUNCT
esrj-50337	127	2	a	a	X
esrj-50337	127	3	)	)	PUNCT
esrj-50337	127	4	wind	wind	NOUN
esrj-50337	127	5	speed	speed	NOUN
esrj-50337	127	6	ws	ws	PROPN
esrj-50337	127	7	/	/	SYM
esrj-50337	127	8	ms-1	ms-1	PROPN
esrj-50337	127	9	,	,	PUNCT
esrj-50337	127	10	(	(	PUNCT
esrj-50337	127	11	b	b	X
esrj-50337	127	12	)	)	PUNCT
esrj-50337	127	13	air	air	NOUN
esrj-50337	127	14	temperature	temperature	NOUN
esrj-50337	127	15	t	t	PROPN
esrj-50337	127	16	/	/	SYM
esrj-50337	127	17	k	k	PROPN
esrj-50337	127	18	,	,	PUNCT
esrj-50337	127	19	and	and	CCONJ
esrj-50337	127	20	(	(	PUNCT
esrj-50337	127	21	c	c	NOUN
esrj-50337	127	22	)	)	PUNCT
esrj-50337	127	23	relative	relative	ADJ
esrj-50337	127	24	humidity	humidity	NOUN
esrj-50337	127	25	rh/%	rh/%	NOUN
esrj-50337	127	26	.	.	PUNCT
esrj-50337	128	1	33long	33long	NUM
esrj-50337	128	2	-	-	PUNCT
esrj-50337	128	3	term	term	NOUN
esrj-50337	128	4	prediction	prediction	NOUN
esrj-50337	128	5	of	of	ADP
esrj-50337	128	6	wind	wind	NOUN
esrj-50337	128	7	speed	speed	NOUN
esrj-50337	128	8	in	in	ADP
esrj-50337	128	9	la	la	PRON
esrj-50337	128	10	serena	serena	PROPN
esrj-50337	128	11	city	city	PROPN
esrj-50337	128	12	(	(	PUNCT
esrj-50337	128	13	chile	chile	PROPN
esrj-50337	128	14	)	)	PUNCT
esrj-50337	128	15	using	use	VERB
esrj-50337	128	16	hybrid	hybrid	ADJ
esrj-50337	128	17	neural	neural	ADJ
esrj-50337	128	18	network	network	NOUN
esrj-50337	128	19	-	-	PUNCT
esrj-50337	128	20	particle	particle	NOUN
esrj-50337	128	21	swarm	swarm	NOUN
esrj-50337	128	22	algorithm	algorithm	NOUN
esrj-50337	128	23	ws	ws	NOUN
esrj-50337	128	24	and	and	CCONJ
esrj-50337	128	25	the	the	DET
esrj-50337	128	26	input	input	NOUN
esrj-50337	128	27	parameters	parameter	NOUN
esrj-50337	128	28	for	for	ADP
esrj-50337	128	29	this	this	DET
esrj-50337	128	30	climate	climate	NOUN
esrj-50337	128	31	zone	zone	NOUN
esrj-50337	128	32	.	.	PUNCT
esrj-50337	129	1	then	then	ADV
esrj-50337	129	2	,	,	PUNCT
esrj-50337	129	3	the	the	DET
esrj-50337	129	4	relationship	relationship	NOUN
esrj-50337	129	5	between	between	ADP
esrj-50337	129	6	ws	ws	NOUN
esrj-50337	129	7	and	and	CCONJ
esrj-50337	129	8	these	these	DET
esrj-50337	129	9	meteorological	meteorological	ADJ
esrj-50337	129	10	data	datum	NOUN
esrj-50337	129	11	is	be	AUX
esrj-50337	129	12	highly	highly	ADV
esrj-50337	129	13	non	non	ADJ
esrj-50337	129	14	-	-	ADJ
esrj-50337	129	15	linear	linear	ADJ
esrj-50337	129	16	,	,	PUNCT
esrj-50337	129	17	and	and	CCONJ
esrj-50337	129	18	consequently	consequently	ADV
esrj-50337	129	19	an	an	DET
esrj-50337	129	20	ann	ann	PROPN
esrj-50337	129	21	is	be	AUX
esrj-50337	129	22	the	the	DET
esrj-50337	129	23	best	good	ADJ
esrj-50337	129	24	alternative	alternative	NOUN
esrj-50337	129	25	to	to	PART
esrj-50337	129	26	model	model	VERB
esrj-50337	129	27	the	the	DET
esrj-50337	129	28	hourly	hourly	ADJ
esrj-50337	129	29	ws	ws	NOUN
esrj-50337	129	30	.	.	PUNCT
esrj-50337	130	1	two	two	NUM
esrj-50337	130	2	new	new	ADJ
esrj-50337	130	3	cases	case	NOUN
esrj-50337	130	4	were	be	AUX
esrj-50337	130	5	studied	study	VERB
esrj-50337	130	6	with	with	ADP
esrj-50337	130	7	this	this	DET
esrj-50337	130	8	methodology	methodology	NOUN
esrj-50337	130	9	,	,	PUNCT
esrj-50337	130	10	the	the	DET
esrj-50337	130	11	long	long	ADJ
esrj-50337	130	12	-	-	PUNCT
esrj-50337	130	13	term	term	NOUN
esrj-50337	130	14	prediction	prediction	NOUN
esrj-50337	130	15	of	of	ADP
esrj-50337	130	16	the	the	DET
esrj-50337	130	17	next	next	ADJ
esrj-50337	130	18	24	24	NUM
esrj-50337	130	19	hours	hour	NOUN
esrj-50337	130	20	ws(t+24	ws(t+24	NUM
esrj-50337	130	21	)	)	PUNCT
esrj-50337	130	22	and	and	CCONJ
esrj-50337	130	23	the	the	DET
esrj-50337	130	24	long	long	ADJ
esrj-50337	130	25	-	-	PUNCT
esrj-50337	130	26	term	term	NOUN
esrj-50337	130	27	prediction	prediction	NOUN
esrj-50337	130	28	of	of	ADP
esrj-50337	130	29	the	the	DET
esrj-50337	130	30	next	next	ADJ
esrj-50337	130	31	48	48	NUM
esrj-50337	130	32	hour	hour	NOUN
esrj-50337	130	33	ws(t+48	ws(t+48	NUM
esrj-50337	130	34	)	)	PUNCT
esrj-50337	130	35	.	.	PUNCT
esrj-50337	131	1	to	to	PART
esrj-50337	131	2	select	select	VERB
esrj-50337	131	3	the	the	DET
esrj-50337	131	4	best	good	ADJ
esrj-50337	131	5	input	input	NOUN
esrj-50337	131	6	parameters	parameter	NOUN
esrj-50337	131	7	for	for	ADP
esrj-50337	131	8	solving	solve	VERB
esrj-50337	131	9	the	the	DET
esrj-50337	131	10	problem	problem	NOUN
esrj-50337	131	11	,	,	PUNCT
esrj-50337	131	12	the	the	DET
esrj-50337	131	13	hourly	hourly	ADJ
esrj-50337	131	14	data	datum	NOUN
esrj-50337	131	15	from	from	ADP
esrj-50337	131	16	the	the	DET
esrj-50337	131	17	current	current	ADJ
esrj-50337	131	18	value	value	NOUN
esrj-50337	131	19	to	to	ADP
esrj-50337	131	20	23	23	NUM
esrj-50337	131	21	past	past	ADJ
esrj-50337	131	22	hours	hour	NOUN
esrj-50337	131	23	(	(	PUNCT
esrj-50337	131	24	t-23	t-23	NOUN
esrj-50337	131	25	,	,	PUNCT
esrj-50337	131	26	t-22	t-22	NUM
esrj-50337	131	27	,	,	PUNCT
esrj-50337	131	28	t-21	t-21	PROPN
esrj-50337	131	29	,	,	PUNCT
esrj-50337	131	30	…	…	PUNCT
esrj-50337	131	31	,	,	PUNCT
esrj-50337	131	32	t	t	PROPN
esrj-50337	131	33	)	)	PUNCT
esrj-50337	131	34	,	,	PUNCT
esrj-50337	131	35	were	be	AUX
esrj-50337	131	36	considered	consider	VERB
esrj-50337	131	37	.	.	PUNCT
esrj-50337	132	1	then	then	ADV
esrj-50337	132	2	,	,	PUNCT
esrj-50337	132	3	the	the	DET
esrj-50337	132	4	sum	sum	NOUN
esrj-50337	132	5	of	of	ADP
esrj-50337	132	6	absolute	absolute	ADJ
esrj-50337	132	7	values	value	NOUN
esrj-50337	132	8	of	of	ADP
esrj-50337	132	9	weights	weight	NOUN
esrj-50337	132	10	(	(	PUNCT
esrj-50337	132	11	savw	savw	ADJ
esrj-50337	132	12	)	)	PUNCT
esrj-50337	132	13	was	be	AUX
esrj-50337	132	14	used	use	VERB
esrj-50337	132	15	(	(	PUNCT
esrj-50337	132	16	lazzús	lazzús	PROPN
esrj-50337	132	17	,	,	PUNCT
esrj-50337	132	18	2013	2013	NUM
esrj-50337	132	19	)	)	PUNCT
esrj-50337	132	20	.	.	PUNCT
esrj-50337	133	1	figure	figure	NOUN
esrj-50337	133	2	4	4	NUM
esrj-50337	133	3	shows	show	VERB
esrj-50337	133	4	the	the	DET
esrj-50337	133	5	input	input	NOUN
esrj-50337	133	6	with	with	ADP
esrj-50337	133	7	more	more	ADJ
esrj-50337	133	8	contribution	contribution	NOUN
esrj-50337	133	9	for	for	ADP
esrj-50337	133	10	the	the	DET
esrj-50337	133	11	prediction	prediction	NOUN
esrj-50337	133	12	of	of	ADP
esrj-50337	133	13	future	future	ADJ
esrj-50337	133	14	values	value	NOUN
esrj-50337	133	15	of	of	ADP
esrj-50337	133	16	ws	ws	NOUN
esrj-50337	133	17	.	.	PUNCT
esrj-50337	134	1	this	this	DET
esrj-50337	134	2	figure	figure	NOUN
esrj-50337	134	3	shows	show	VERB
esrj-50337	134	4	the	the	DET
esrj-50337	134	5	great	great	ADJ
esrj-50337	134	6	significance	significance	NOUN
esrj-50337	134	7	of	of	ADP
esrj-50337	134	8	the	the	DET
esrj-50337	134	9	past	past	ADJ
esrj-50337	134	10	values	value	NOUN
esrj-50337	134	11	(	(	PUNCT
esrj-50337	134	12	t-18	t-18	NOUN
esrj-50337	134	13	)	)	PUNCT
esrj-50337	134	14	,	,	PUNCT
esrj-50337	134	15	(	(	PUNCT
esrj-50337	134	16	t-12	t-12	NOUN
esrj-50337	134	17	)	)	PUNCT
esrj-50337	134	18	,	,	PUNCT
esrj-50337	134	19	(	(	PUNCT
esrj-50337	134	20	t-6	t-6	NOUN
esrj-50337	134	21	)	)	PUNCT
esrj-50337	134	22	,	,	PUNCT
esrj-50337	134	23	and	and	CCONJ
esrj-50337	134	24	the	the	DET
esrj-50337	134	25	current	current	ADJ
esrj-50337	134	26	value	value	NOUN
esrj-50337	134	27	(	(	PUNCT
esrj-50337	134	28	t	t	NOUN
esrj-50337	134	29	)	)	PUNCT
esrj-50337	134	30	on	on	ADP
esrj-50337	134	31	the	the	DET
esrj-50337	134	32	three	three	NUM
esrj-50337	134	33	meteorological	meteorological	ADJ
esrj-50337	134	34	data	datum	NOUN
esrj-50337	134	35	(	(	PUNCT
esrj-50337	134	36	ws	ws	PROPN
esrj-50337	134	37	,	,	PUNCT
esrj-50337	134	38	t	t	PROPN
esrj-50337	134	39	,	,	PUNCT
esrj-50337	134	40	and	and	CCONJ
esrj-50337	134	41	rh	rh	PROPN
esrj-50337	134	42	)	)	PUNCT
esrj-50337	134	43	.	.	PUNCT
esrj-50337	135	1	figure	figure	VERB
esrj-50337	135	2	3	3	NUM
esrj-50337	135	3	.	.	PUNCT
esrj-50337	136	1	experimental	experimental	ADJ
esrj-50337	136	2	data	datum	NOUN
esrj-50337	136	3	of	of	ADP
esrj-50337	136	4	ws	ws	NOUN
esrj-50337	136	5	from	from	ADP
esrj-50337	136	6	2003	2003	NUM
esrj-50337	136	7	to	to	ADP
esrj-50337	136	8	2007	2007	NUM
esrj-50337	136	9	as	as	ADP
esrj-50337	136	10	a	a	DET
esrj-50337	136	11	function	function	NOUN
esrj-50337	136	12	of	of	ADP
esrj-50337	136	13	the	the	DET
esrj-50337	136	14	selected	select	VERB
esrj-50337	136	15	meteorological	meteorological	ADJ
esrj-50337	136	16	parameters	parameter	NOUN
esrj-50337	136	17	.	.	PUNCT
esrj-50337	137	1	a	a	DET
esrj-50337	137	2	)	)	PUNCT
esrj-50337	137	3	ws	ws	NOUN
esrj-50337	137	4	vs	vs	ADP
esrj-50337	137	5	t	t	PROPN
esrj-50337	137	6	→r2=0.5979	→r2=0.5979	ADP
esrj-50337	137	7	;	;	PUNCT
esrj-50337	137	8	(	(	PUNCT
esrj-50337	137	9	b	b	X
esrj-50337	137	10	)	)	PUNCT
esrj-50337	137	11	ws	ws	NOUN
esrj-50337	137	12	vs	vs	ADP
esrj-50337	137	13	rh	rh	PROPN
esrj-50337	137	14	→r2=0.2757	→r2=0.2757	PROPN
esrj-50337	137	15	.	.	PUNCT
esrj-50337	138	1	thus	thus	ADV
esrj-50337	138	2	,	,	PUNCT
esrj-50337	138	3	the	the	DET
esrj-50337	138	4	optimum	optimum	ADJ
esrj-50337	138	5	input	input	NOUN
esrj-50337	138	6	vector	vector	NOUN
esrj-50337	138	7	was	be	AUX
esrj-50337	138	8	:	:	PUNCT
esrj-50337	138	9	(	(	PUNCT
esrj-50337	138	10	16	16	NUM
esrj-50337	138	11	)	)	PUNCT
esrj-50337	138	12	where	where	SCONJ
esrj-50337	138	13	τ	τ	PROPN
esrj-50337	138	14	is	be	AUX
esrj-50337	138	15	the	the	DET
esrj-50337	138	16	future	future	ADJ
esrj-50337	138	17	value	value	NOUN
esrj-50337	138	18	to	to	PART
esrj-50337	138	19	predict	predict	VERB
esrj-50337	138	20	:	:	PUNCT
esrj-50337	138	21	24	24	NUM
esrj-50337	138	22	and	and	CCONJ
esrj-50337	138	23	48	48	NUM
esrj-50337	138	24	for	for	ADP
esrj-50337	138	25	the	the	DET
esrj-50337	138	26	long	long	ADJ
esrj-50337	138	27	-	-	PUNCT
esrj-50337	138	28	term	term	NOUN
esrj-50337	138	29	prediction	prediction	NOUN
esrj-50337	138	30	.	.	PUNCT
esrj-50337	139	1	figure	figure	NOUN
esrj-50337	139	2	4	4	NUM
esrj-50337	139	3	.	.	PUNCT
esrj-50337	140	1	influence	influence	NOUN
esrj-50337	140	2	of	of	ADP
esrj-50337	140	3	several	several	ADJ
esrj-50337	140	4	points	point	NOUN
esrj-50337	140	5	of	of	ADP
esrj-50337	140	6	time	time	NOUN
esrj-50337	140	7	series	series	NOUN
esrj-50337	140	8	on	on	ADP
esrj-50337	140	9	prediction	prediction	NOUN
esrj-50337	140	10	of	of	ADP
esrj-50337	140	11	future	future	ADJ
esrj-50337	140	12	values	value	NOUN
esrj-50337	140	13	of	of	ADP
esrj-50337	140	14	wind	wind	NOUN
esrj-50337	140	15	speed	speed	NOUN
esrj-50337	140	16	.	.	PUNCT
esrj-50337	141	1	bars	bar	NOUN
esrj-50337	141	2	represent	represent	VERB
esrj-50337	141	3	the	the	DET
esrj-50337	141	4	sumsof	sumsof	NOUN
esrj-50337	141	5	absolute	absolute	ADJ
esrj-50337	141	6	values	value	NOUN
esrj-50337	141	7	of	of	ADP
esrj-50337	141	8	weights	weight	NOUN
esrj-50337	141	9	(	(	PUNCT
esrj-50337	141	10	savw	savw	ADJ
esrj-50337	141	11	)	)	PUNCT
esrj-50337	141	12	of	of	ADP
esrj-50337	141	13	the	the	DET
esrj-50337	141	14	ann+pso	ann+pso	PROPN
esrj-50337	141	15	.	.	PUNCT
esrj-50337	142	1	the	the	DET
esrj-50337	142	2	leave-20%-out	leave-20%-out	NOUN
esrj-50337	142	3	cross	cross	ADJ
esrj-50337	142	4	-	-	ADJ
esrj-50337	142	5	validation	validation	ADJ
esrj-50337	142	6	method	method	NOUN
esrj-50337	142	7	was	be	AUX
esrj-50337	142	8	used	use	VERB
esrj-50337	142	9	to	to	PART
esrj-50337	142	10	estimate	estimate	VERB
esrj-50337	142	11	the	the	DET
esrj-50337	142	12	predictive	predictive	ADJ
esrj-50337	142	13	capabilities	capability	NOUN
esrj-50337	142	14	of	of	ADP
esrj-50337	142	15	the	the	DET
esrj-50337	142	16	model	model	NOUN
esrj-50337	142	17	.	.	PUNCT
esrj-50337	143	1	34996	34996	NUM
esrj-50337	143	2	data	datum	NOUN
esrj-50337	143	3	points	point	NOUN
esrj-50337	143	4	were	be	AUX
esrj-50337	143	5	used	use	VERB
esrj-50337	143	6	in	in	ADP
esrj-50337	143	7	the	the	DET
esrj-50337	143	8	training	training	NOUN
esrj-50337	143	9	set	set	NOUN
esrj-50337	143	10	,	,	PUNCT
esrj-50337	143	11	and	and	CCONJ
esrj-50337	143	12	8760	8760	NUM
esrj-50337	143	13	data	datum	NOUN
esrj-50337	143	14	points	point	NOUN
esrj-50337	143	15	(	(	PUNCT
esrj-50337	143	16	not	not	PART
esrj-50337	143	17	used	use	VERB
esrj-50337	143	18	in	in	ADP
esrj-50337	143	19	the	the	DET
esrj-50337	143	20	training	training	NOUN
esrj-50337	143	21	step	step	NOUN
esrj-50337	143	22	)	)	PUNCT
esrj-50337	143	23	were	be	AUX
esrj-50337	143	24	used	use	VERB
esrj-50337	143	25	in	in	ADP
esrj-50337	143	26	the	the	DET
esrj-50337	143	27	prediction	prediction	NOUN
esrj-50337	143	28	set	set	NOUN
esrj-50337	143	29	.	.	PUNCT
esrj-50337	144	1	4	4	X
esrj-50337	144	2	.	.	NOUN
esrj-50337	144	3	results	result	NOUN
esrj-50337	144	4	and	and	CCONJ
esrj-50337	144	5	discussion	discussion	NOUN
esrj-50337	144	6	several	several	ADJ
esrj-50337	144	7	network	network	NOUN
esrj-50337	144	8	architectures	architecture	NOUN
esrj-50337	144	9	were	be	AUX
esrj-50337	144	10	tested	test	VERB
esrj-50337	144	11	for	for	ADP
esrj-50337	144	12	the	the	DET
esrj-50337	144	13	long	long	ADJ
esrj-50337	144	14	-	-	PUNCT
esrj-50337	144	15	term	term	NOUN
esrj-50337	144	16	wind	wind	NOUN
esrj-50337	144	17	speed	speed	NOUN
esrj-50337	144	18	prediction	prediction	NOUN
esrj-50337	144	19	(	(	PUNCT
esrj-50337	144	20	t(t+24	t(t+24	NOUN
esrj-50337	144	21	)	)	PUNCT
esrj-50337	144	22	and	and	CCONJ
esrj-50337	144	23	t(t+48	t(t+48	VERB
esrj-50337	144	24	)	)	PUNCT
esrj-50337	144	25	,	,	PUNCT
esrj-50337	144	26	separately	separately	ADV
esrj-50337	144	27	)	)	PUNCT
esrj-50337	144	28	.	.	PUNCT
esrj-50337	145	1	the	the	DET
esrj-50337	145	2	optimum	optimum	ADJ
esrj-50337	145	3	architecture	architecture	NOUN
esrj-50337	145	4	was	be	AUX
esrj-50337	145	5	checked	check	VERB
esrj-50337	145	6	using	use	VERB
esrj-50337	145	7	the	the	DET
esrj-50337	145	8	objective	objective	ADJ
esrj-50337	145	9	function	function	NOUN
esrj-50337	145	10	(	(	PUNCT
esrj-50337	145	11	eq	eq	NOUN
esrj-50337	145	12	.	.	PROPN
esrj-50337	145	13	10	10	NUM
esrj-50337	145	14	)	)	PUNCT
esrj-50337	145	15	.	.	PUNCT
esrj-50337	146	1	figure	figure	NOUN
esrj-50337	146	2	5	5	NUM
esrj-50337	146	3	shows	show	NOUN
esrj-50337	146	4	mse	mse	PROPN
esrj-50337	146	5	found	find	VERB
esrj-50337	146	6	in	in	ADP
esrj-50337	146	7	correlating	correlate	VERB
esrj-50337	146	8	the	the	DET
esrj-50337	146	9	ws	ws	NOUN
esrj-50337	146	10	as	as	ADP
esrj-50337	146	11	function	function	NOUN
esrj-50337	146	12	of	of	ADP
esrj-50337	146	13	the	the	DET
esrj-50337	146	14	number	number	NOUN
esrj-50337	146	15	of	of	ADP
esrj-50337	146	16	neurons	neuron	NOUN
esrj-50337	146	17	in	in	ADP
esrj-50337	146	18	the	the	DET
esrj-50337	146	19	hidden	hide	VERB
esrj-50337	146	20	layer	layer	NOUN
esrj-50337	146	21	(	(	PUNCT
esrj-50337	146	22	nn	nn	NOUN
esrj-50337	146	23	)	)	PUNCT
esrj-50337	146	24	.	.	PUNCT
esrj-50337	147	1	fig	fig	NOUN
esrj-50337	147	2	.	.	PUNCT
esrj-50337	148	1	5a	5a	NUM
esrj-50337	148	2	shows	show	VERB
esrj-50337	148	3	the	the	DET
esrj-50337	148	4	best	good	ADJ
esrj-50337	148	5	topology	topology	NOUN
esrj-50337	148	6	found	find	VERB
esrj-50337	148	7	for	for	ADP
esrj-50337	148	8	the	the	DET
esrj-50337	148	9	prediction	prediction	NOUN
esrj-50337	148	10	of	of	ADP
esrj-50337	148	11	the	the	DET
esrj-50337	148	12	ws	ws	NOUN
esrj-50337	148	13	for	for	ADP
esrj-50337	148	14	the	the	DET
esrj-50337	148	15	next	next	ADJ
esrj-50337	148	16	24	24	NUM
esrj-50337	148	17	hours	hour	NOUN
esrj-50337	148	18	t(t+24	t(t+24	PROPN
esrj-50337	148	19	)	)	PUNCT
esrj-50337	148	20	with	with	ADP
esrj-50337	148	21	a	a	DET
esrj-50337	148	22	network	network	NOUN
esrj-50337	148	23	architecture	architecture	NOUN
esrj-50337	148	24	12	12	NUM
esrj-50337	148	25	-	-	SYM
esrj-50337	148	26	28	28	NUM
esrj-50337	148	27	-	-	SYM
esrj-50337	148	28	1	1	NUM
esrj-50337	148	29	.	.	PUNCT
esrj-50337	148	30	fig	fig	NOUN
esrj-50337	148	31	.	.	PUNCT
esrj-50337	149	1	5b	5b	PROPN
esrj-50337	149	2	shows	show	VERB
esrj-50337	149	3	the	the	DET
esrj-50337	149	4	prediction	prediction	NOUN
esrj-50337	149	5	of	of	ADP
esrj-50337	149	6	the	the	DET
esrj-50337	149	7	ws	ws	NOUN
esrj-50337	149	8	for	for	ADP
esrj-50337	149	9	the	the	DET
esrj-50337	149	10	next	next	ADJ
esrj-50337	149	11	48	48	NUM
esrj-50337	149	12	hours	hour	NOUN
esrj-50337	149	13	t(t+48	t(t+48	VERB
esrj-50337	149	14	)	)	PUNCT
esrj-50337	149	15	with	with	ADP
esrj-50337	149	16	an	an	DET
esrj-50337	149	17	optimum	optimum	ADJ
esrj-50337	149	18	network	network	NOUN
esrj-50337	149	19	architecture	architecture	NOUN
esrj-50337	149	20	of	of	ADP
esrj-50337	149	21	12	12	NUM
esrj-50337	149	22	-	-	SYM
esrj-50337	149	23	42	42	NUM
esrj-50337	149	24	-	-	SYM
esrj-50337	149	25	1	1	NUM
esrj-50337	149	26	.	.	PUNCT
esrj-50337	150	1	once	once	ADV
esrj-50337	150	2	the	the	DET
esrj-50337	150	3	best	good	ADJ
esrj-50337	150	4	architectures	architecture	NOUN
esrj-50337	150	5	were	be	AUX
esrj-50337	150	6	determined	determine	VERB
esrj-50337	150	7	,	,	PUNCT
esrj-50337	150	8	the	the	DET
esrj-50337	150	9	optimum	optimum	ADJ
esrj-50337	150	10	weights	weight	NOUN
esrj-50337	150	11	and	and	CCONJ
esrj-50337	150	12	biases	bias	NOUN
esrj-50337	150	13	required	require	VERB
esrj-50337	150	14	to	to	PART
esrj-50337	150	15	carry	carry	VERB
esrj-50337	150	16	out	out	ADP
esrj-50337	150	17	the	the	DET
esrj-50337	150	18	estimate	estimate	NOUN
esrj-50337	150	19	of	of	ADP
esrj-50337	150	20	future	future	ADJ
esrj-50337	150	21	values	value	NOUN
esrj-50337	150	22	of	of	ADP
esrj-50337	150	23	ws	ws	NOUN
esrj-50337	150	24	were	be	AUX
esrj-50337	150	25	obtained	obtain	VERB
esrj-50337	150	26	.	.	PUNCT
esrj-50337	151	1	figure	figure	NOUN
esrj-50337	151	2	6	6	NUM
esrj-50337	151	3	shows	show	VERB
esrj-50337	151	4	a	a	DET
esrj-50337	151	5	comparison	comparison	NOUN
esrj-50337	151	6	between	between	ADP
esrj-50337	151	7	real	real	ADJ
esrj-50337	151	8	data	datum	NOUN
esrj-50337	151	9	(	(	PUNCT
esrj-50337	151	10	black	black	ADJ
esrj-50337	151	11	line	line	NOUN
esrj-50337	151	12	)	)	PUNCT
esrj-50337	151	13	and	and	CCONJ
esrj-50337	151	14	calculated	calculate	VERB
esrj-50337	151	15	values	value	NOUN
esrj-50337	151	16	(	(	PUNCT
esrj-50337	151	17	points	point	NOUN
esrj-50337	151	18	)	)	PUNCT
esrj-50337	151	19	of	of	ADP
esrj-50337	151	20	long	long	ADJ
esrj-50337	151	21	-	-	PUNCT
esrj-50337	151	22	term	term	NOUN
esrj-50337	151	23	prediction	prediction	NOUN
esrj-50337	151	24	of	of	ADP
esrj-50337	151	25	ws(t+24	ws(t+24	NOUN
esrj-50337	151	26	)	)	PUNCT
esrj-50337	151	27	.	.	PUNCT
esrj-50337	152	1	fig	fig	NOUN
esrj-50337	152	2	.	.	PUNCT
esrj-50337	153	1	6a	6a	NOUN
esrj-50337	153	2	shows	show	VERB
esrj-50337	153	3	the	the	DET
esrj-50337	153	4	forecasting	forecasting	NOUN
esrj-50337	153	5	of	of	ADP
esrj-50337	153	6	ws(t+24	ws(t+24	PUNCT
esrj-50337	153	7	)	)	PUNCT
esrj-50337	153	8	with	with	ADP
esrj-50337	153	9	a	a	DET
esrj-50337	153	10	correlation	correlation	NOUN
esrj-50337	153	11	coefficient	coefficient	NOUN
esrj-50337	153	12	(	(	PUNCT
esrj-50337	153	13	r2	r2	PROPN
esrj-50337	153	14	)	)	PUNCT
esrj-50337	153	15	of	of	ADP
esrj-50337	153	16	0.974	0.974	NUM
esrj-50337	153	17	for	for	ADP
esrj-50337	153	18	the	the	DET
esrj-50337	153	19	training	training	NOUN
esrj-50337	153	20	set	set	NOUN
esrj-50337	153	21	(	(	PUNCT
esrj-50337	153	22	years	year	NOUN
esrj-50337	153	23	2003	2003	NUM
esrj-50337	153	24	-	-	SYM
esrj-50337	153	25	2006	2006	NUM
esrj-50337	153	26	)	)	PUNCT
esrj-50337	153	27	with	with	ADP
esrj-50337	153	28	a	a	DET
esrj-50337	153	29	slope	slope	NOUN
esrj-50337	153	30	of	of	ADP
esrj-50337	153	31	the	the	DET
esrj-50337	153	32	curve	curve	NOUN
esrj-50337	153	33	(	(	PUNCT
esrj-50337	153	34	m	m	NOUN
esrj-50337	153	35	)	)	PUNCT
esrj-50337	153	36	of	of	ADP
esrj-50337	153	37	0.965	0.965	NUM
esrj-50337	153	38	(	(	PUNCT
esrj-50337	153	39	expected	expect	VERB
esrj-50337	153	40	to	to	PART
esrj-50337	153	41	be	be	AUX
esrj-50337	153	42	1.0	1.0	NUM
esrj-50337	153	43	)	)	PUNCT
esrj-50337	153	44	,	,	PUNCT
esrj-50337	153	45	and	and	CCONJ
esrj-50337	153	46	with	with	ADP
esrj-50337	153	47	rmse=0.787	rmse=0.787	NOUN
esrj-50337	153	48	[	[	X
esrj-50337	153	49	m·s−1	m·s−1	NOUN
esrj-50337	153	50	]	]	X
esrj-50337	153	51	(	(	PUNCT
esrj-50337	153	52	mse=0.620	mse=0.620	NOUN
esrj-50337	153	53	[	[	X
esrj-50337	153	54	m·s−1]2	m·s−1]2	NOUN
esrj-50337	153	55	and	and	CCONJ
esrj-50337	153	56	msemax=10.028	msemax=10.028	NOUN
esrj-50337	153	57	[	[	X
esrj-50337	153	58	m·s-1]2	m·s-1]2	NUM
esrj-50337	153	59	)	)	PUNCT
esrj-50337	153	60	.	.	PUNCT
esrj-50337	154	1	fig	fig	NOUN
esrj-50337	154	2	.	.	PUNCT
esrj-50337	155	1	6b	6b	PROPN
esrj-50337	155	2	shows	show	VERB
esrj-50337	155	3	the	the	DET
esrj-50337	155	4	forecasting	forecasting	NOUN
esrj-50337	155	5	of	of	ADP
esrj-50337	155	6	ws(t+24	ws(t+24	PUNCT
esrj-50337	155	7	)	)	PUNCT
esrj-50337	155	8	with	with	ADP
esrj-50337	155	9	r2=0.973	r2=0.973	NUM
esrj-50337	155	10	during	during	ADP
esrj-50337	155	11	the	the	DET
esrj-50337	155	12	prediction	prediction	NOUN
esrj-50337	155	13	step	step	NOUN
esrj-50337	155	14	(	(	PUNCT
esrj-50337	155	15	year	year	NOUN
esrj-50337	155	16	2007	2007	NUM
esrj-50337	155	17	)	)	PUNCT
esrj-50337	155	18	with	with	ADP
esrj-50337	155	19	m=0.963	m=0.963	PROPN
esrj-50337	155	20	(	(	PUNCT
esrj-50337	155	21	also	also	ADV
esrj-50337	155	22	expected	expect	VERB
esrj-50337	155	23	to	to	PART
esrj-50337	155	24	be	be	AUX
esrj-50337	155	25	1.0	1.0	NUM
esrj-50337	155	26	)	)	PUNCT
esrj-50337	155	27	,	,	PUNCT
esrj-50337	155	28	and	and	CCONJ
esrj-50337	155	29	with	with	ADP
esrj-50337	155	30	rmse=0.807	rmse=0.807	NOUN
esrj-50337	155	31	[	[	X
esrj-50337	155	32	m·s−1	m·s−1	NOUN
esrj-50337	155	33	]	]	X
esrj-50337	155	34	(	(	PUNCT
esrj-50337	155	35	mse=0.651	mse=0.651	PROPN
esrj-50337	155	36	[	[	X
esrj-50337	155	37	m·s−1]2	m·s−1]2	ADJ
esrj-50337	155	38	and	and	CCONJ
esrj-50337	155	39	msemax=7.110	msemax=7.110	NOUN
esrj-50337	155	40	[	[	X
esrj-50337	155	41	m·s−1]2	m·s−1]2	NOUN
esrj-50337	155	42	)	)	PUNCT
esrj-50337	155	43	.	.	PUNCT
esrj-50337	156	1	figure	figure	NOUN
esrj-50337	156	2	5	5	NUM
esrj-50337	156	3	.	.	PUNCT
esrj-50337	157	1	deviations	deviation	NOUN
esrj-50337	157	2	found	find	VERB
esrj-50337	157	3	in	in	ADP
esrj-50337	157	4	the	the	DET
esrj-50337	157	5	correlation	correlation	NOUN
esrj-50337	157	6	of	of	ADP
esrj-50337	157	7	wind	wind	NOUN
esrj-50337	157	8	speed	speed	NOUN
esrj-50337	157	9	as	as	ADP
esrj-50337	157	10	a	a	DET
esrj-50337	157	11	function	function	NOUN
esrj-50337	157	12	of	of	ADP
esrj-50337	157	13	the	the	DET
esrj-50337	157	14	number	number	NOUN
esrj-50337	157	15	of	of	ADP
esrj-50337	157	16	neurons	neuron	NOUN
esrj-50337	157	17	in	in	ADP
esrj-50337	157	18	the	the	DET
esrj-50337	157	19	hidden	hide	VERB
esrj-50337	157	20	layer	layer	NOUN
esrj-50337	157	21	for	for	ADP
esrj-50337	157	22	:	:	PUNCT
esrj-50337	157	23	(	(	PUNCT
esrj-50337	157	24	a	a	X
esrj-50337	157	25	)	)	PUNCT
esrj-50337	157	26	long	long	ADJ
esrj-50337	157	27	-	-	PUNCT
esrj-50337	157	28	term	term	NOUN
esrj-50337	157	29	prediction	prediction	NOUN
esrj-50337	157	30	ws(t+24	ws(t+24	NUM
esrj-50337	157	31	)	)	PUNCT
esrj-50337	157	32	,	,	PUNCT
esrj-50337	157	33	and	and	CCONJ
esrj-50337	157	34	(	(	PUNCT
esrj-50337	157	35	b	b	NOUN
esrj-50337	157	36	)	)	PUNCT
esrj-50337	157	37	long	long	ADJ
esrj-50337	157	38	-	-	PUNCT
esrj-50337	157	39	term	term	NOUN
esrj-50337	157	40	prediction	prediction	NOUN
esrj-50337	157	41	ws(t+48	ws(t+48	NUM
esrj-50337	157	42	)	)	PUNCT
esrj-50337	157	43	.	.	PUNCT
esrj-50337	158	1	in	in	ADP
esrj-50337	158	2	both	both	DET
esrj-50337	158	3	graphics	graphic	NOUN
esrj-50337	158	4	,	,	PUNCT
esrj-50337	158	5	training	training	NOUN
esrj-50337	158	6	step	step	NOUN
esrj-50337	158	7	(	(	PUNCT
esrj-50337	158	8	■	■	NOUN
esrj-50337	158	9	)	)	PUNCT
esrj-50337	158	10	and	and	CCONJ
esrj-50337	158	11	prediction	prediction	NOUN
esrj-50337	158	12	step	step	NOUN
esrj-50337	158	13	(	(	PUNCT
esrj-50337	158	14	○	○	PROPN
esrj-50337	158	15	)	)	PUNCT
esrj-50337	158	16	.	.	PUNCT
esrj-50337	159	1	34	34	NUM
esrj-50337	159	2	juan	juan	PROPN
esrj-50337	159	3	a.	a.	PROPN
esrj-50337	159	4	lazzús	lazzús	PROPN
esrj-50337	159	5	and	and	CCONJ
esrj-50337	159	6	ignacio	ignacio	PROPN
esrj-50337	159	7	salfate	salfate	PROPN
esrj-50337	159	8	figure	figure	NOUN
esrj-50337	159	9	6	6	NUM
esrj-50337	159	10	.	.	PUNCT
esrj-50337	159	11	comparison	comparison	NOUN
esrj-50337	159	12	between	between	ADP
esrj-50337	159	13	real	real	ADJ
esrj-50337	159	14	and	and	CCONJ
esrj-50337	159	15	calculated	calculated	ADJ
esrj-50337	159	16	values	value	NOUN
esrj-50337	159	17	of	of	ADP
esrj-50337	159	18	ws(t+24	ws(t+24	X
esrj-50337	159	19	)	)	PUNCT
esrj-50337	159	20	using	use	VERB
esrj-50337	159	21	the	the	DET
esrj-50337	159	22	proposed	propose	VERB
esrj-50337	159	23	ann+pso	ann+pso	PROPN
esrj-50337	159	24	model	model	NOUN
esrj-50337	159	25	.	.	PUNCT
esrj-50337	160	1	(	(	PUNCT
esrj-50337	160	2	a	a	X
esrj-50337	160	3	)	)	PUNCT
esrj-50337	160	4	training	training	NOUN
esrj-50337	160	5	set	set	NOUN
esrj-50337	160	6	,	,	PUNCT
esrj-50337	160	7	period	period	NOUN
esrj-50337	160	8	2003	2003	NUM
esrj-50337	160	9	-	-	SYM
esrj-50337	160	10	2006	2006	NUM
esrj-50337	160	11	;	;	PUNCT
esrj-50337	160	12	and	and	CCONJ
esrj-50337	160	13	(	(	PUNCT
esrj-50337	160	14	b	b	NOUN
esrj-50337	160	15	)	)	PUNCT
esrj-50337	160	16	prediction	prediction	NOUN
esrj-50337	160	17	set	set	NOUN
esrj-50337	160	18	,	,	PUNCT
esrj-50337	160	19	period	period	NOUN
esrj-50337	160	20	2007	2007	NUM
esrj-50337	160	21	.	.	PUNCT
esrj-50337	161	1	figure	figure	NOUN
esrj-50337	161	2	7	7	NUM
esrj-50337	161	3	shows	show	VERB
esrj-50337	161	4	a	a	DET
esrj-50337	161	5	comparison	comparison	NOUN
esrj-50337	161	6	between	between	ADP
esrj-50337	161	7	real	real	ADJ
esrj-50337	161	8	data	datum	NOUN
esrj-50337	161	9	(	(	PUNCT
esrj-50337	161	10	black	black	ADJ
esrj-50337	161	11	line	line	NOUN
esrj-50337	161	12	)	)	PUNCT
esrj-50337	161	13	and	and	CCONJ
esrj-50337	161	14	calculated	calculate	VERB
esrj-50337	161	15	values	value	NOUN
esrj-50337	161	16	(	(	PUNCT
esrj-50337	161	17	points	point	NOUN
esrj-50337	161	18	)	)	PUNCT
esrj-50337	161	19	of	of	ADP
esrj-50337	161	20	long	long	ADJ
esrj-50337	161	21	-	-	PUNCT
esrj-50337	161	22	term	term	NOUN
esrj-50337	161	23	prediction	prediction	NOUN
esrj-50337	161	24	of	of	ADP
esrj-50337	161	25	ws(t+48	ws(t+48	NUM
esrj-50337	161	26	)	)	PUNCT
esrj-50337	161	27	.	.	PUNCT
esrj-50337	162	1	fig	fig	NOUN
esrj-50337	162	2	.	.	PUNCT
esrj-50337	163	1	6a	6a	NOUN
esrj-50337	163	2	shows	show	VERB
esrj-50337	163	3	the	the	DET
esrj-50337	163	4	forecasting	forecasting	NOUN
esrj-50337	163	5	of	of	ADP
esrj-50337	163	6	ws(t+48	ws(t+48	NUM
esrj-50337	163	7	)	)	PUNCT
esrj-50337	163	8	with	with	ADP
esrj-50337	163	9	r2=0.970	r2=0.970	PRON
esrj-50337	163	10	for	for	ADP
esrj-50337	163	11	the	the	DET
esrj-50337	163	12	training	training	NOUN
esrj-50337	163	13	set	set	VERB
esrj-50337	163	14	with	with	ADP
esrj-50337	163	15	m=0.963	m=0.963	PROPN
esrj-50337	163	16	,	,	PUNCT
esrj-50337	163	17	and	and	CCONJ
esrj-50337	163	18	with	with	ADP
esrj-50337	163	19	rmse=0.788	rmse=0.788	NOUN
esrj-50337	163	20	[	[	X
esrj-50337	163	21	m·s−1	m·s−1	NOUN
esrj-50337	163	22	]	]	X
esrj-50337	163	23	(	(	PUNCT
esrj-50337	163	24	mse=0.622	mse=0.622	PROPN
esrj-50337	164	1	[	[	X
esrj-50337	164	2	m·s−1]2	m·s−1]2	PROPN
esrj-50337	164	3	and	and	CCONJ
esrj-50337	164	4	msemax=9.386	msemax=9.386	NUM
esrj-50337	164	5	[	[	X
esrj-50337	164	6	m·s−1]2	m·s−1]2	NOUN
esrj-50337	164	7	)	)	PUNCT
esrj-50337	164	8	.	.	PUNCT
esrj-50337	165	1	fig	fig	NOUN
esrj-50337	165	2	.	.	PUNCT
esrj-50337	166	1	6b	6b	PROPN
esrj-50337	166	2	shows	show	VERB
esrj-50337	166	3	the	the	DET
esrj-50337	166	4	forecasting	forecasting	NOUN
esrj-50337	166	5	of	of	ADP
esrj-50337	166	6	ws(t+48	ws(t+48	NUM
esrj-50337	166	7	)	)	PUNCT
esrj-50337	166	8	with	with	ADP
esrj-50337	166	9	r2=0.969	r2=0.969	NOUN
esrj-50337	166	10	for	for	ADP
esrj-50337	166	11	the	the	DET
esrj-50337	166	12	prediction	prediction	NOUN
esrj-50337	166	13	set	set	VERB
esrj-50337	166	14	with	with	ADP
esrj-50337	166	15	m=0.962	m=0.962	NUM
esrj-50337	166	16	,	,	PUNCT
esrj-50337	166	17	and	and	CCONJ
esrj-50337	166	18	with	with	ADP
esrj-50337	166	19	rmse=0.796	rmse=0.796	PRON
esrj-50337	166	20	(	(	PUNCT
esrj-50337	166	21	mse=0.634	mse=0.634	PROPN
esrj-50337	166	22	[	[	X
esrj-50337	166	23	m·s−1]2	m·s−1]2	NOUN
esrj-50337	166	24	and	and	CCONJ
esrj-50337	166	25	msemax=8.210	msemax=8.210	NOUN
esrj-50337	167	1	[	[	X
esrj-50337	167	2	m·s−1]2	m·s−1]2	NOUN
esrj-50337	167	3	)	)	PUNCT
esrj-50337	167	4	.	.	PUNCT
esrj-50337	168	1	a	a	DET
esrj-50337	168	2	comparison	comparison	NOUN
esrj-50337	168	3	was	be	AUX
esrj-50337	168	4	made	make	VERB
esrj-50337	168	5	with	with	ADP
esrj-50337	168	6	a	a	DET
esrj-50337	168	7	neural	neural	ADJ
esrj-50337	168	8	network	network	NOUN
esrj-50337	168	9	with	with	ADP
esrj-50337	168	10	standard	standard	ADJ
esrj-50337	168	11	back	back	ADJ
esrj-50337	168	12	-	-	PUNCT
esrj-50337	168	13	propagation	propagation	NOUN
esrj-50337	168	14	(	(	PUNCT
esrj-50337	168	15	bpnn	bpnn	NOUN
esrj-50337	168	16	)	)	PUNCT
esrj-50337	168	17	algorithm	algorithm	NOUN
esrj-50337	168	18	(	(	PUNCT
esrj-50337	168	19	hagan	hagan	NOUN
esrj-50337	168	20	and	and	CCONJ
esrj-50337	168	21	menhaj	menhaj	NOUN
esrj-50337	168	22	,	,	PUNCT
esrj-50337	168	23	1994	1994	NUM
esrj-50337	168	24	)	)	PUNCT
esrj-50337	168	25	,	,	PUNCT
esrj-50337	168	26	and	and	CCONJ
esrj-50337	168	27	similar	similar	ADJ
esrj-50337	168	28	architecture	architecture	NOUN
esrj-50337	168	29	and	and	CCONJ
esrj-50337	168	30	database	database	NOUN
esrj-50337	168	31	.	.	PUNCT
esrj-50337	169	1	this	this	DET
esrj-50337	169	2	bpnn	bpnn	NOUN
esrj-50337	169	3	show	show	VERB
esrj-50337	169	4	results	result	NOUN
esrj-50337	169	5	of	of	ADP
esrj-50337	169	6	mse	mse	NOUN
esrj-50337	169	7	higher	high	ADJ
esrj-50337	169	8	than	than	ADP
esrj-50337	169	9	1	1	NUM
esrj-50337	169	10	[	[	X
esrj-50337	169	11	m·s−1]2	m·s−1]2	ADJ
esrj-50337	169	12	and	and	CCONJ
esrj-50337	169	13	r2	r2	NOUN
esrj-50337	169	14	lower	lower	ADV
esrj-50337	169	15	0.8	0.8	NUM
esrj-50337	169	16	for	for	ADP
esrj-50337	169	17	the	the	DET
esrj-50337	169	18	forecasting	forecasting	NOUN
esrj-50337	169	19	of	of	ADP
esrj-50337	169	20	ws(t+24	ws(t+24	X
esrj-50337	169	21	)	)	PUNCT
esrj-50337	169	22	and	and	CCONJ
esrj-50337	169	23	ws(t+48	ws(t+48	NUM
esrj-50337	169	24	)	)	PUNCT
esrj-50337	169	25	.	.	PUNCT
esrj-50337	170	1	and	and	CCONJ
esrj-50337	170	2	other	other	ADJ
esrj-50337	170	3	comparison	comparison	NOUN
esrj-50337	170	4	was	be	AUX
esrj-50337	170	5	made	make	VERB
esrj-50337	170	6	with	with	ADP
esrj-50337	170	7	a	a	DET
esrj-50337	170	8	multiple	multiple	ADJ
esrj-50337	170	9	linear	linear	ADJ
esrj-50337	170	10	regression	regression	NOUN
esrj-50337	170	11	(	(	PUNCT
esrj-50337	170	12	mlr	mlr	NOUN
esrj-50337	170	13	)	)	PUNCT
esrj-50337	170	14	method	method	NOUN
esrj-50337	170	15	,	,	PUNCT
esrj-50337	170	16	and	and	CCONJ
esrj-50337	170	17	similar	similar	ADJ
esrj-50337	170	18	database	database	NOUN
esrj-50337	170	19	.	.	PUNCT
esrj-50337	171	1	the	the	DET
esrj-50337	171	2	mlr	mlr	PROPN
esrj-50337	171	3	method	method	NOUN
esrj-50337	171	4	shows	show	VERB
esrj-50337	171	5	mse	mse	PROPN
esrj-50337	171	6	higher	high	ADJ
esrj-50337	171	7	than	than	ADP
esrj-50337	171	8	4	4	NUM
esrj-50337	171	9	[	[	X
esrj-50337	171	10	m·s−1]2	m·s−1]2	ADJ
esrj-50337	171	11	and	and	CCONJ
esrj-50337	171	12	r2	r2	PROPN
esrj-50337	171	13	lower	lower	ADV
esrj-50337	171	14	0.7	0.7	NUM
esrj-50337	171	15	for	for	ADP
esrj-50337	171	16	both	both	DET
esrj-50337	171	17	cases	case	NOUN
esrj-50337	171	18	.	.	PUNCT
esrj-50337	172	1	note	note	VERB
esrj-50337	172	2	that	that	SCONJ
esrj-50337	172	3	,	,	PUNCT
esrj-50337	172	4	the	the	DET
esrj-50337	172	5	predictions	prediction	NOUN
esrj-50337	172	6	with	with	ADP
esrj-50337	172	7	the	the	DET
esrj-50337	172	8	proposed	proposed	ADJ
esrj-50337	172	9	ann+	ann+	PROPN
esrj-50337	172	10	pso	pso	NOUN
esrj-50337	172	11	method	method	NOUN
esrj-50337	172	12	shows	show	VERB
esrj-50337	172	13	mse	mse	PROPN
esrj-50337	172	14	a	a	DET
esrj-50337	172	15	little	little	ADV
esrj-50337	172	16	higher	high	ADJ
esrj-50337	172	17	than	than	ADP
esrj-50337	172	18	0.6	0.6	NUM
esrj-50337	173	1	[	[	X
esrj-50337	173	2	m·s−1]2	m·s−1]2	ADJ
esrj-50337	173	3	and	and	CCONJ
esrj-50337	173	4	r2	r2	NOUN
esrj-50337	173	5	higher	high	ADJ
esrj-50337	173	6	than	than	ADP
esrj-50337	173	7	0.97	0.97	NUM
esrj-50337	173	8	.	.	PUNCT
esrj-50337	174	1	table	table	NOUN
esrj-50337	174	2	4	4	NUM
esrj-50337	174	3	summarizes	summarize	NOUN
esrj-50337	174	4	the	the	DET
esrj-50337	174	5	deviations	deviation	NOUN
esrj-50337	174	6	obtained	obtain	VERB
esrj-50337	174	7	in	in	ADP
esrj-50337	174	8	the	the	DET
esrj-50337	174	9	long	long	ADJ
esrj-50337	174	10	-	-	PUNCT
esrj-50337	174	11	term	term	NOUN
esrj-50337	174	12	prediction	prediction	NOUN
esrj-50337	174	13	using	use	VERB
esrj-50337	174	14	the	the	DET
esrj-50337	174	15	proposed	propose	VERB
esrj-50337	174	16	method	method	NOUN
esrj-50337	174	17	versus	versus	ADP
esrj-50337	174	18	bpnn	bpnn	NOUN
esrj-50337	174	19	and	and	CCONJ
esrj-50337	174	20	mlr	mlr	NOUN
esrj-50337	174	21	methods	method	NOUN
esrj-50337	174	22	.	.	PUNCT
esrj-50337	175	1	these	these	DET
esrj-50337	175	2	results	result	NOUN
esrj-50337	175	3	show	show	VERB
esrj-50337	175	4	that	that	SCONJ
esrj-50337	175	5	the	the	DET
esrj-50337	175	6	ann+pso	ann+pso	NOUN
esrj-50337	175	7	can	can	AUX
esrj-50337	175	8	be	be	AUX
esrj-50337	175	9	accurately	accurately	ADV
esrj-50337	175	10	trained	train	VERB
esrj-50337	175	11	and	and	CCONJ
esrj-50337	175	12	that	that	SCONJ
esrj-50337	175	13	the	the	DET
esrj-50337	175	14	chosen	choose	VERB
esrj-50337	175	15	topologies	topology	NOUN
esrj-50337	175	16	can	can	AUX
esrj-50337	175	17	estimate	estimate	VERB
esrj-50337	175	18	the	the	DET
esrj-50337	175	19	future	future	ADJ
esrj-50337	175	20	values	value	NOUN
esrj-50337	175	21	of	of	ADP
esrj-50337	175	22	ws	ws	NOUN
esrj-50337	175	23	with	with	ADP
esrj-50337	175	24	acceptable	acceptable	ADJ
esrj-50337	175	25	accuracy	accuracy	NOUN
esrj-50337	175	26	.	.	PUNCT
esrj-50337	176	1	these	these	DET
esrj-50337	176	2	results	result	NOUN
esrj-50337	176	3	represent	represent	VERB
esrj-50337	176	4	a	a	DET
esrj-50337	176	5	tremendous	tremendous	ADJ
esrj-50337	176	6	increase	increase	NOUN
esrj-50337	176	7	in	in	ADP
esrj-50337	176	8	accuracy	accuracy	NOUN
esrj-50337	176	9	for	for	ADP
esrj-50337	176	10	forecasting	forecast	VERB
esrj-50337	176	11	this	this	DET
esrj-50337	176	12	important	important	ADJ
esrj-50337	176	13	meteorological	meteorological	ADJ
esrj-50337	176	14	property	property	NOUN
esrj-50337	176	15	and	and	CCONJ
esrj-50337	176	16	show	show	VERB
esrj-50337	176	17	that	that	SCONJ
esrj-50337	176	18	not	not	PART
esrj-50337	176	19	only	only	ADV
esrj-50337	176	20	the	the	DET
esrj-50337	176	21	optimum	optimum	ADJ
esrj-50337	176	22	architecture	architecture	NOUN
esrj-50337	176	23	obtained	obtain	VERB
esrj-50337	176	24	was	be	AUX
esrj-50337	176	25	crucial	crucial	ADJ
esrj-50337	176	26	,	,	PUNCT
esrj-50337	176	27	also	also	ADV
esrj-50337	176	28	the	the	DET
esrj-50337	176	29	appropriate	appropriate	ADJ
esrj-50337	176	30	selection	selection	NOUN
esrj-50337	176	31	of	of	ADP
esrj-50337	176	32	the	the	DET
esrj-50337	176	33	independent	independent	ADJ
esrj-50337	176	34	parameters	parameter	NOUN
esrj-50337	176	35	(	(	PUNCT
esrj-50337	176	36	t	t	PROPN
esrj-50337	176	37	and	and	CCONJ
esrj-50337	176	38	rh	rh	PROPN
esrj-50337	176	39	)	)	PUNCT
esrj-50337	176	40	.	.	PUNCT
esrj-50337	177	1	this	this	PRON
esrj-50337	177	2	is	be	AUX
esrj-50337	177	3	important	important	ADJ
esrj-50337	177	4	because	because	SCONJ
esrj-50337	177	5	air	air	NOUN
esrj-50337	177	6	temperature	temperature	NOUN
esrj-50337	177	7	and	and	CCONJ
esrj-50337	177	8	relative	relative	ADJ
esrj-50337	177	9	humidity	humidity	NOUN
esrj-50337	177	10	are	be	AUX
esrj-50337	177	11	commonly	commonly	ADV
esrj-50337	177	12	available	available	ADJ
esrj-50337	177	13	parameters	parameter	NOUN
esrj-50337	177	14	.	.	PUNCT
esrj-50337	178	1	note	note	VERB
esrj-50337	178	2	that	that	SCONJ
esrj-50337	178	3	the	the	DET
esrj-50337	178	4	coefficients	coefficient	NOUN
esrj-50337	178	5	of	of	ADP
esrj-50337	178	6	linear	linear	ADJ
esrj-50337	178	7	correlation	correlation	NOUN
esrj-50337	178	8	of	of	ADP
esrj-50337	178	9	these	these	DET
esrj-50337	178	10	parameters	parameter	NOUN
esrj-50337	178	11	show	show	VERB
esrj-50337	178	12	a	a	DET
esrj-50337	178	13	non	non	ADJ
esrj-50337	178	14	-	-	ADJ
esrj-50337	178	15	linear	linear	ADJ
esrj-50337	178	16	relationship	relationship	NOUN
esrj-50337	178	17	with	with	ADP
esrj-50337	178	18	the	the	DET
esrj-50337	178	19	ws	ws	NOUN
esrj-50337	178	20	for	for	ADP
esrj-50337	178	21	the	the	DET
esrj-50337	178	22	climate	climate	NOUN
esrj-50337	178	23	of	of	ADP
esrj-50337	178	24	several	several	ADJ
esrj-50337	178	25	geographic	geographic	ADJ
esrj-50337	178	26	zones	zone	NOUN
esrj-50337	178	27	.	.	PUNCT
esrj-50337	179	1	then	then	ADV
esrj-50337	179	2	,	,	PUNCT
esrj-50337	179	3	the	the	DET
esrj-50337	179	4	relationship	relationship	NOUN
esrj-50337	179	5	between	between	ADP
esrj-50337	179	6	ws	ws	NOUN
esrj-50337	179	7	and	and	CCONJ
esrj-50337	179	8	these	these	DET
esrj-50337	179	9	meteorological	meteorological	ADJ
esrj-50337	179	10	data	datum	NOUN
esrj-50337	179	11	is	be	AUX
esrj-50337	179	12	highly	highly	ADV
esrj-50337	179	13	non	non	ADJ
esrj-50337	179	14	-	-	ADJ
esrj-50337	179	15	linear	linear	ADJ
esrj-50337	179	16	,	,	PUNCT
esrj-50337	179	17	and	and	CCONJ
esrj-50337	179	18	consequently	consequently	ADV
esrj-50337	179	19	the	the	DET
esrj-50337	179	20	ann+pso	ann+pso	PROPN
esrj-50337	179	21	is	be	AUX
esrj-50337	179	22	a	a	DET
esrj-50337	179	23	good	good	ADJ
esrj-50337	179	24	tool	tool	NOUN
esrj-50337	179	25	for	for	ADP
esrj-50337	179	26	modeling	model	VERB
esrj-50337	179	27	ws	ws	NOUN
esrj-50337	179	28	for	for	ADP
esrj-50337	179	29	several	several	ADJ
esrj-50337	179	30	applications	application	NOUN
esrj-50337	179	31	.	.	PUNCT
esrj-50337	180	1	figure	figure	NOUN
esrj-50337	180	2	6	6	NUM
esrj-50337	180	3	.	.	PUNCT
esrj-50337	180	4	comparison	comparison	NOUN
esrj-50337	180	5	between	between	ADP
esrj-50337	180	6	real	real	ADJ
esrj-50337	180	7	and	and	CCONJ
esrj-50337	180	8	calculated	calculated	ADJ
esrj-50337	180	9	values	value	NOUN
esrj-50337	180	10	of	of	ADP
esrj-50337	180	11	ws(t+48	ws(t+48	NUM
esrj-50337	180	12	)	)	PUNCT
esrj-50337	180	13	using	use	VERB
esrj-50337	180	14	the	the	DET
esrj-50337	180	15	proposed	propose	VERB
esrj-50337	180	16	ann+pso	ann+pso	PROPN
esrj-50337	180	17	model	model	NOUN
esrj-50337	180	18	.	.	PUNCT
esrj-50337	181	1	(	(	PUNCT
esrj-50337	181	2	a	a	X
esrj-50337	181	3	)	)	PUNCT
esrj-50337	181	4	training	training	NOUN
esrj-50337	181	5	set	set	NOUN
esrj-50337	181	6	,	,	PUNCT
esrj-50337	181	7	period	period	NOUN
esrj-50337	181	8	2003	2003	NUM
esrj-50337	181	9	-	-	SYM
esrj-50337	181	10	2006	2006	NUM
esrj-50337	181	11	;	;	PUNCT
esrj-50337	181	12	and	and	CCONJ
esrj-50337	181	13	(	(	PUNCT
esrj-50337	181	14	b	b	NOUN
esrj-50337	181	15	)	)	PUNCT
esrj-50337	181	16	prediction	prediction	NOUN
esrj-50337	181	17	set	set	NOUN
esrj-50337	181	18	,	,	PUNCT
esrj-50337	181	19	period	period	NOUN
esrj-50337	181	20	2007	2007	NUM
esrj-50337	181	21	.	.	PUNCT
esrj-50337	182	1	the	the	DET
esrj-50337	182	2	results	result	NOUN
esrj-50337	182	3	obtained	obtain	VERB
esrj-50337	182	4	by	by	ADP
esrj-50337	182	5	other	other	ADJ
esrj-50337	182	6	models	model	NOUN
esrj-50337	182	7	and	and	CCONJ
esrj-50337	182	8	other	other	ADJ
esrj-50337	182	9	sites	site	NOUN
esrj-50337	182	10	can	can	AUX
esrj-50337	182	11	be	be	AUX
esrj-50337	182	12	usefully	usefully	ADV
esrj-50337	182	13	compared	compare	VERB
esrj-50337	182	14	with	with	ADP
esrj-50337	182	15	our	our	PRON
esrj-50337	182	16	results	result	NOUN
esrj-50337	182	17	.	.	PUNCT
esrj-50337	183	1	zhang	zhang	PROPN
esrj-50337	183	2	et	et	PROPN
esrj-50337	183	3	al	al	PROPN
esrj-50337	183	4	.	.	PROPN
esrj-50337	183	5	(	(	PUNCT
esrj-50337	183	6	2012	2012	NUM
esrj-50337	183	7	)	)	PUNCT
esrj-50337	183	8	shows	show	VERB
esrj-50337	183	9	the	the	DET
esrj-50337	183	10	performance	performance	NOUN
esrj-50337	183	11	analysis	analysis	NOUN
esrj-50337	183	12	of	of	ADP
esrj-50337	183	13	four	four	NUM
esrj-50337	183	14	modified	modified	ADJ
esrj-50337	183	15	approaches	approach	NOUN
esrj-50337	183	16	for	for	ADP
esrj-50337	183	17	wind	wind	NOUN
esrj-50337	183	18	speed	speed	NOUN
esrj-50337	183	19	forecasting	forecasting	NOUN
esrj-50337	183	20	for	for	ADP
esrj-50337	183	21	four	four	NUM
esrj-50337	183	22	observation	observation	NOUN
esrj-50337	183	23	sites	site	NOUN
esrj-50337	183	24	in	in	ADP
esrj-50337	183	25	gansu	gansu	PROPN
esrj-50337	183	26	(	(	PUNCT
esrj-50337	183	27	china	china	PROPN
esrj-50337	183	28	)	)	PUNCT
esrj-50337	183	29	,	,	PUNCT
esrj-50337	183	30	with	with	ADP
esrj-50337	183	31	mse	mse	PROPN
esrj-50337	183	32	higher	high	ADJ
esrj-50337	183	33	than	than	ADP
esrj-50337	183	34	2	2	NUM
esrj-50337	183	35	[	[	X
esrj-50337	183	36	m·s−1]2	m·s−1]2	NOUN
esrj-50337	183	37	.	.	PUNCT
esrj-50337	184	1	liu	liu	PROPN
esrj-50337	184	2	et	et	PROPN
esrj-50337	184	3	al	al	PROPN
esrj-50337	184	4	.	.	PROPN
esrj-50337	185	1	(	(	PUNCT
esrj-50337	185	2	2013	2013	NUM
esrj-50337	185	3	)	)	PUNCT
esrj-50337	185	4	shows	show	VERB
esrj-50337	185	5	the	the	DET
esrj-50337	185	6	forecasting	forecasting	NOUN
esrj-50337	185	7	models	model	NOUN
esrj-50337	185	8	for	for	ADP
esrj-50337	185	9	wind	wind	NOUN
esrj-50337	185	10	speed	speed	NOUN
esrj-50337	185	11	using	use	VERB
esrj-50337	185	12	wavelet	wavelet	NOUN
esrj-50337	185	13	,	,	PUNCT
esrj-50337	185	14	wavelet	wavelet	NOUN
esrj-50337	185	15	packet	packet	NOUN
esrj-50337	185	16	,	,	PUNCT
esrj-50337	185	17	time	time	NOUN
esrj-50337	185	18	series	series	NOUN
esrj-50337	185	19	and	and	CCONJ
esrj-50337	185	20	artificial	artificial	ADJ
esrj-50337	185	21	neural	neural	ADJ
esrj-50337	185	22	networks	network	NOUN
esrj-50337	185	23	with	with	ADP
esrj-50337	185	24	mse	mse	PROPN
esrj-50337	185	25	higher	high	ADJ
esrj-50337	185	26	than	than	ADP
esrj-50337	185	27	1	1	NUM
esrj-50337	185	28	[	[	X
esrj-50337	185	29	m·s−1]2	m·s−1]2	NOUN
esrj-50337	185	30	.	.	PUNCT
esrj-50337	186	1	for	for	ADP
esrj-50337	186	2	spain	spain	PROPN
esrj-50337	186	3	,	,	PUNCT
esrj-50337	186	4	the	the	DET
esrj-50337	186	5	wind	wind	NOUN
esrj-50337	186	6	speed	speed	NOUN
esrj-50337	186	7	estimation	estimation	NOUN
esrj-50337	186	8	was	be	AUX
esrj-50337	186	9	made	make	VERB
esrj-50337	186	10	using	use	VERB
esrj-50337	186	11	a	a	DET
esrj-50337	186	12	multilayer	multilayer	ADJ
esrj-50337	186	13	perceptron	perceptron	NOUN
esrj-50337	186	14	with	with	ADP
esrj-50337	186	15	mse	mse	PROPN
esrj-50337	186	16	higher	high	ADJ
esrj-50337	186	17	than	than	ADP
esrj-50337	186	18	1	1	NUM
esrj-50337	187	1	[	[	X
esrj-50337	187	2	m·s−1]2	m·s−1]2	ADJ
esrj-50337	187	3	and	and	CCONJ
esrj-50337	187	4	r2	r2	PROPN
esrj-50337	187	5	below	below	ADP
esrj-50337	187	6	0.75	0.75	NUM
esrj-50337	187	7	(	(	PUNCT
esrj-50337	187	8	velo	velo	PROPN
esrj-50337	187	9	et	et	PROPN
esrj-50337	187	10	al	al	PROPN
esrj-50337	187	11	.	.	PROPN
esrj-50337	187	12	,	,	PUNCT
esrj-50337	187	13	2014	2014	NUM
esrj-50337	187	14	)	)	PUNCT
esrj-50337	187	15	.	.	PUNCT
esrj-50337	188	1	recently	recently	ADV
esrj-50337	188	2	,	,	PUNCT
esrj-50337	188	3	wang	wang	PROPN
esrj-50337	188	4	et	et	PROPN
esrj-50337	188	5	al	al	PROPN
esrj-50337	188	6	.	.	PROPN
esrj-50337	188	7	(	(	PUNCT
esrj-50337	188	8	2014	2014	NUM
esrj-50337	188	9	)	)	PUNCT
esrj-50337	188	10	shows	show	VERB
esrj-50337	188	11	the	the	DET
esrj-50337	188	12	mean	mean	ADJ
esrj-50337	188	13	hourly	hourly	ADJ
esrj-50337	188	14	wind	wind	NOUN
esrj-50337	188	15	speed	speed	NOUN
esrj-50337	188	16	prediction	prediction	NOUN
esrj-50337	188	17	in	in	ADP
esrj-50337	188	18	the	the	DET
esrj-50337	188	19	hexi	hexi	PROPN
esrj-50337	188	20	corridor	corridor	PROPN
esrj-50337	188	21	of	of	ADP
esrj-50337	188	22	china	china	PROPN
esrj-50337	188	23	based	base	VERB
esrj-50337	188	24	on	on	ADP
esrj-50337	188	25	the	the	DET
esrj-50337	188	26	seasonal	seasonal	ADJ
esrj-50337	188	27	adjustment	adjustment	NOUN
esrj-50337	188	28	method	method	NOUN
esrj-50337	188	29	(	(	PUNCT
esrj-50337	188	30	sam	sam	PROPN
esrj-50337	188	31	)	)	PUNCT
esrj-50337	188	32	,	,	PUNCT
esrj-50337	188	33	exponential	exponential	ADJ
esrj-50337	188	34	smoothing	smoothing	NOUN
esrj-50337	188	35	method	method	NOUN
esrj-50337	188	36	(	(	PUNCT
esrj-50337	188	37	esm	esm	PROPN
esrj-50337	188	38	)	)	PUNCT
esrj-50337	188	39	,	,	PUNCT
esrj-50337	188	40	and	and	CCONJ
esrj-50337	188	41	radial	radial	ADJ
esrj-50337	188	42	basis	basis	NOUN
esrj-50337	188	43	function	function	NOUN
esrj-50337	188	44	neural	neural	ADJ
esrj-50337	188	45	network	network	NOUN
esrj-50337	188	46	(	(	PUNCT
esrj-50337	188	47	rbfn	rbfn	NOUN
esrj-50337	188	48	)	)	PUNCT
esrj-50337	188	49	,	,	PUNCT
esrj-50337	188	50	with	with	ADP
esrj-50337	188	51	rsme	rsme	NOUN
esrj-50337	188	52	higher	higher	ADV
esrj-50337	188	53	than	than	ADP
esrj-50337	188	54	0.7	0.7	NUM
esrj-50337	188	55	[	[	X
esrj-50337	188	56	m·s−1	m·s−1	NOUN
esrj-50337	188	57	]	]	PUNCT
esrj-50337	188	58	.	.	PUNCT
esrj-50337	189	1	it	it	PRON
esrj-50337	189	2	must	must	AUX
esrj-50337	189	3	be	be	AUX
esrj-50337	189	4	mentioned	mention	VERB
esrj-50337	189	5	that	that	SCONJ
esrj-50337	189	6	these	these	DET
esrj-50337	189	7	results	result	NOUN
esrj-50337	189	8	were	be	AUX
esrj-50337	189	9	obtained	obtain	VERB
esrj-50337	189	10	from	from	ADP
esrj-50337	189	11	different	different	ADJ
esrj-50337	189	12	sites	site	NOUN
esrj-50337	189	13	and	and	CCONJ
esrj-50337	189	14	based	base	VERB
esrj-50337	189	15	on	on	ADP
esrj-50337	189	16	different	different	ADJ
esrj-50337	189	17	weather	weather	NOUN
esrj-50337	189	18	conditions	condition	NOUN
esrj-50337	189	19	,	,	PUNCT
esrj-50337	189	20	and	and	CCONJ
esrj-50337	189	21	the	the	DET
esrj-50337	189	22	results	result	NOUN
esrj-50337	189	23	can	can	AUX
esrj-50337	189	24	not	not	PART
esrj-50337	189	25	be	be	AUX
esrj-50337	189	26	compared	compare	VERB
esrj-50337	189	27	directly	directly	ADV
esrj-50337	189	28	with	with	ADP
esrj-50337	189	29	one	one	NUM
esrj-50337	189	30	another	another	DET
esrj-50337	189	31	.	.	PUNCT
esrj-50337	190	1	however	however	ADV
esrj-50337	190	2	,	,	PUNCT
esrj-50337	190	3	results	result	NOUN
esrj-50337	190	4	from	from	ADP
esrj-50337	190	5	the	the	DET
esrj-50337	190	6	different	different	ADJ
esrj-50337	190	7	methods	method	NOUN
esrj-50337	190	8	show	show	VERB
esrj-50337	190	9	that	that	SCONJ
esrj-50337	190	10	the	the	DET
esrj-50337	190	11	accuracy	accuracy	NOUN
esrj-50337	190	12	of	of	ADP
esrj-50337	190	13	ann+pso	ann+pso	PROPN
esrj-50337	190	14	model	model	NOUN
esrj-50337	190	15	employed	employ	VERB
esrj-50337	190	16	in	in	ADP
esrj-50337	190	17	this	this	DET
esrj-50337	190	18	study	study	NOUN
esrj-50337	190	19	is	be	AUX
esrj-50337	190	20	good	good	ADJ
esrj-50337	190	21	.	.	PUNCT
esrj-50337	191	1	5	5	X
esrj-50337	191	2	.	.	X
esrj-50337	191	3	conclusions	conclusion	NOUN
esrj-50337	191	4	in	in	ADP
esrj-50337	191	5	this	this	DET
esrj-50337	191	6	work	work	NOUN
esrj-50337	191	7	,	,	PUNCT
esrj-50337	191	8	a	a	DET
esrj-50337	191	9	neural	neural	ADJ
esrj-50337	191	10	network	network	NOUN
esrj-50337	191	11	was	be	AUX
esrj-50337	191	12	used	use	VERB
esrj-50337	191	13	for	for	ADP
esrj-50337	191	14	the	the	DET
esrj-50337	191	15	forecasting	forecasting	NOUN
esrj-50337	191	16	of	of	ADP
esrj-50337	191	17	longterm	longterm	ADJ
esrj-50337	191	18	wind	wind	NOUN
esrj-50337	191	19	speed	speed	NOUN
esrj-50337	191	20	time	time	NOUN
esrj-50337	191	21	series	series	NOUN
esrj-50337	191	22	.	.	PUNCT
esrj-50337	192	1	in	in	ADP
esrj-50337	192	2	order	order	NOUN
esrj-50337	192	3	to	to	PART
esrj-50337	192	4	obtain	obtain	VERB
esrj-50337	192	5	a	a	DET
esrj-50337	192	6	more	more	ADV
esrj-50337	192	7	effective	effective	ADJ
esrj-50337	192	8	correlation	correlation	NOUN
esrj-50337	192	9	and	and	CCONJ
esrj-50337	192	10	prediction	prediction	NOUN
esrj-50337	192	11	,	,	PUNCT
esrj-50337	192	12	particle	particle	NOUN
esrj-50337	192	13	swarm	swarm	NOUN
esrj-50337	192	14	algorithm	algorithm	NOUN
esrj-50337	192	15	has	have	AUX
esrj-50337	192	16	been	be	AUX
esrj-50337	192	17	introduced	introduce	VERB
esrj-50337	192	18	to	to	PART
esrj-50337	192	19	update	update	VERB
esrj-50337	192	20	the	the	DET
esrj-50337	192	21	weights	weight	NOUN
esrj-50337	192	22	of	of	ADP
esrj-50337	192	23	all	all	DET
esrj-50337	192	24	layers	layer	NOUN
esrj-50337	192	25	of	of	ADP
esrj-50337	192	26	the	the	DET
esrj-50337	192	27	network	network	NOUN
esrj-50337	192	28	.	.	PUNCT
esrj-50337	193	1	43800	43800	NUM
esrj-50337	193	2	data	datum	NOUN
esrj-50337	193	3	points	point	NOUN
esrj-50337	193	4	(	(	PUNCT
esrj-50337	193	5	years	year	NOUN
esrj-50337	193	6	2003table	2003table	NUM
esrj-50337	193	7	4	4	NUM
esrj-50337	193	8	.	.	PUNCT
esrj-50337	194	1	summary	summary	NOUN
esrj-50337	194	2	of	of	ADP
esrj-50337	194	3	the	the	DET
esrj-50337	194	4	deviations	deviation	NOUN
esrj-50337	194	5	obtained	obtain	VERB
esrj-50337	194	6	with	with	ADP
esrj-50337	194	7	the	the	DET
esrj-50337	194	8	ann+pso	ann+pso	PROPN
esrj-50337	194	9	algorithm	algorithm	NOUN
esrj-50337	194	10	for	for	ADP
esrj-50337	194	11	the	the	DET
esrj-50337	194	12	long	long	ADJ
esrj-50337	194	13	-	-	PUNCT
esrj-50337	194	14	term	term	NOUN
esrj-50337	194	15	prediction	prediction	NOUN
esrj-50337	194	16	of	of	ADP
esrj-50337	194	17	wind	wind	NOUN
esrj-50337	194	18	speed	speed	NOUN
esrj-50337	194	19	..	..	PUNCT
esrj-50337	194	20	35long	35long	NUM
esrj-50337	194	21	-	-	PUNCT
esrj-50337	194	22	term	term	NOUN
esrj-50337	194	23	prediction	prediction	NOUN
esrj-50337	194	24	of	of	ADP
esrj-50337	194	25	wind	wind	NOUN
esrj-50337	194	26	speed	speed	NOUN
esrj-50337	194	27	in	in	ADP
esrj-50337	194	28	la	la	PRON
esrj-50337	194	29	serena	serena	PROPN
esrj-50337	194	30	city	city	PROPN
esrj-50337	194	31	(	(	PUNCT
esrj-50337	194	32	chile	chile	PROPN
esrj-50337	194	33	)	)	PUNCT
esrj-50337	194	34	using	use	VERB
esrj-50337	194	35	hybrid	hybrid	ADJ
esrj-50337	194	36	neural	neural	ADJ
esrj-50337	194	37	network	network	NOUN
esrj-50337	194	38	-	-	PUNCT
esrj-50337	194	39	particle	particle	NOUN
esrj-50337	194	40	swarm	swarm	NOUN
esrj-50337	194	41	algorithm	algorithm	NOUN
esrj-50337	194	42	2007	2007	NUM
esrj-50337	194	43	)	)	PUNCT
esrj-50337	194	44	of	of	ADP
esrj-50337	194	45	wind	wind	NOUN
esrj-50337	194	46	speed	speed	NOUN
esrj-50337	194	47	were	be	AUX
esrj-50337	194	48	used	use	VERB
esrj-50337	194	49	.	.	PUNCT
esrj-50337	195	1	to	to	PART
esrj-50337	195	2	distinguish	distinguish	VERB
esrj-50337	195	3	between	between	ADP
esrj-50337	195	4	the	the	DET
esrj-50337	195	5	different	different	ADJ
esrj-50337	195	6	values	value	NOUN
esrj-50337	195	7	of	of	ADP
esrj-50337	195	8	hourly	hourly	ADJ
esrj-50337	195	9	data	datum	NOUN
esrj-50337	195	10	considered	consider	VERB
esrj-50337	195	11	in	in	ADP
esrj-50337	195	12	this	this	DET
esrj-50337	195	13	study	study	NOUN
esrj-50337	195	14	,	,	PUNCT
esrj-50337	195	15	so	so	SCONJ
esrj-50337	195	16	that	that	SCONJ
esrj-50337	195	17	the	the	DET
esrj-50337	195	18	network	network	NOUN
esrj-50337	195	19	can	can	AUX
esrj-50337	195	20	discriminate	discriminate	VERB
esrj-50337	195	21	and	and	CCONJ
esrj-50337	195	22	learn	learn	VERB
esrj-50337	195	23	in	in	ADP
esrj-50337	195	24	optimum	optimum	ADJ
esrj-50337	195	25	form	form	NOUN
esrj-50337	195	26	,	,	PUNCT
esrj-50337	195	27	the	the	DET
esrj-50337	195	28	following	follow	VERB
esrj-50337	195	29	past	past	ADJ
esrj-50337	195	30	values	value	NOUN
esrj-50337	195	31	of	of	ADP
esrj-50337	195	32	the	the	DET
esrj-50337	195	33	time	time	NOUN
esrj-50337	195	34	series	series	NOUN
esrj-50337	195	35	of	of	ADP
esrj-50337	195	36	meteorological	meteorological	ADJ
esrj-50337	195	37	data	datum	NOUN
esrj-50337	195	38	were	be	AUX
esrj-50337	195	39	used	use	VERB
esrj-50337	195	40	as	as	ADP
esrj-50337	195	41	input	input	NOUN
esrj-50337	195	42	parameters	parameter	NOUN
esrj-50337	195	43	:	:	PUNCT
esrj-50337	195	44	wind	wind	NOUN
esrj-50337	195	45	speed	speed	NOUN
esrj-50337	195	46	ws(m·s-1	ws(m·s-1	NOUN
esrj-50337	195	47	)	)	PUNCT
esrj-50337	195	48	,	,	PUNCT
esrj-50337	195	49	relative	relative	ADJ
esrj-50337	195	50	humidity	humidity	NOUN
esrj-50337	195	51	rh(%	rh(%	NOUN
esrj-50337	195	52	)	)	PUNCT
esrj-50337	195	53	,	,	PUNCT
esrj-50337	195	54	and	and	CCONJ
esrj-50337	195	55	air	air	NOUN
esrj-50337	195	56	temperature	temperature	NOUN
esrj-50337	195	57	t(k	t(k	PROPN
esrj-50337	195	58	)	)	PUNCT
esrj-50337	195	59	.	.	PUNCT
esrj-50337	196	1	based	base	VERB
esrj-50337	196	2	on	on	ADP
esrj-50337	196	3	the	the	DET
esrj-50337	196	4	results	result	NOUN
esrj-50337	196	5	and	and	CCONJ
esrj-50337	196	6	discussion	discussion	NOUN
esrj-50337	196	7	presented	present	VERB
esrj-50337	196	8	in	in	ADP
esrj-50337	196	9	this	this	DET
esrj-50337	196	10	study	study	NOUN
esrj-50337	196	11	,	,	PUNCT
esrj-50337	196	12	the	the	DET
esrj-50337	196	13	following	follow	VERB
esrj-50337	196	14	main	main	ADJ
esrj-50337	196	15	conclusions	conclusion	NOUN
esrj-50337	196	16	are	be	AUX
esrj-50337	196	17	obtained	obtain	VERB
esrj-50337	196	18	:	:	PUNCT
esrj-50337	196	19	i	i	X
esrj-50337	196	20	)	)	PUNCT
esrj-50337	196	21	the	the	DET
esrj-50337	196	22	results	result	NOUN
esrj-50337	196	23	show	show	VERB
esrj-50337	196	24	that	that	SCONJ
esrj-50337	196	25	the	the	DET
esrj-50337	196	26	proposed	propose	VERB
esrj-50337	196	27	ann+pso	ann+pso	NOUN
esrj-50337	196	28	can	can	AUX
esrj-50337	196	29	be	be	AUX
esrj-50337	196	30	properly	properly	ADV
esrj-50337	196	31	trained	train	VERB
esrj-50337	196	32	for	for	ADP
esrj-50337	196	33	predicting	predict	VERB
esrj-50337	196	34	the	the	DET
esrj-50337	196	35	hourly	hourly	ADJ
esrj-50337	196	36	wind	wind	NOUN
esrj-50337	196	37	speed	speed	NOUN
esrj-50337	196	38	,	,	PUNCT
esrj-50337	196	39	with	with	ADP
esrj-50337	196	40	acceptable	acceptable	ADJ
esrj-50337	196	41	accuracy	accuracy	NOUN
esrj-50337	196	42	;	;	PUNCT
esrj-50337	196	43	ii	ii	X
esrj-50337	196	44	)	)	PUNCT
esrj-50337	196	45	the	the	DET
esrj-50337	196	46	meteorological	meteorological	ADJ
esrj-50337	196	47	variables	variable	NOUN
esrj-50337	196	48	used	use	VERB
esrj-50337	196	49	(	(	PUNCT
esrj-50337	196	50	ws	ws	PROPN
esrj-50337	196	51	,	,	PUNCT
esrj-50337	196	52	t	t	PROPN
esrj-50337	196	53	,	,	PUNCT
esrj-50337	196	54	and	and	CCONJ
esrj-50337	196	55	rh	rh	PROPN
esrj-50337	196	56	)	)	PUNCT
esrj-50337	196	57	,	,	PUNCT
esrj-50337	196	58	have	have	VERB
esrj-50337	196	59	influential	influential	ADJ
esrj-50337	196	60	effects	effect	NOUN
esrj-50337	196	61	,	,	PUNCT
esrj-50337	196	62	on	on	ADP
esrj-50337	196	63	the	the	DET
esrj-50337	196	64	good	good	ADJ
esrj-50337	196	65	training	training	NOUN
esrj-50337	196	66	and	and	CCONJ
esrj-50337	196	67	predicting	predict	VERB
esrj-50337	196	68	capabilities	capability	NOUN
esrj-50337	196	69	,	,	PUNCT
esrj-50337	196	70	of	of	ADP
esrj-50337	196	71	the	the	DET
esrj-50337	196	72	chosen	choose	VERB
esrj-50337	196	73	network	network	NOUN
esrj-50337	196	74	;	;	PUNCT
esrj-50337	196	75	iii	iii	X
esrj-50337	196	76	)	)	PUNCT
esrj-50337	196	77	the	the	DET
esrj-50337	196	78	low	low	ADJ
esrj-50337	196	79	deviations	deviation	NOUN
esrj-50337	196	80	found	find	VERB
esrj-50337	196	81	with	with	ADP
esrj-50337	196	82	the	the	DET
esrj-50337	196	83	proposed	propose	VERB
esrj-50337	196	84	ann+pso	ann+pso	NOUN
esrj-50337	196	85	method	method	NOUN
esrj-50337	196	86	indicate	indicate	VERB
esrj-50337	196	87	that	that	SCONJ
esrj-50337	196	88	it	it	PRON
esrj-50337	196	89	can	can	AUX
esrj-50337	196	90	predict	predict	VERB
esrj-50337	196	91	the	the	DET
esrj-50337	196	92	future	future	ADJ
esrj-50337	196	93	values	value	NOUN
esrj-50337	196	94	of	of	ADP
esrj-50337	196	95	ws	ws	NOUN
esrj-50337	196	96	with	with	ADP
esrj-50337	196	97	better	well	ADJ
esrj-50337	196	98	accuracy	accuracy	NOUN
esrj-50337	196	99	than	than	ADP
esrj-50337	196	100	other	other	ADJ
esrj-50337	196	101	methods	method	NOUN
esrj-50337	196	102	;	;	PUNCT
esrj-50337	196	103	and	and	CCONJ
esrj-50337	196	104	iv	iv	X
esrj-50337	196	105	)	)	PUNCT
esrj-50337	196	106	the	the	DET
esrj-50337	196	107	values	value	NOUN
esrj-50337	196	108	obtained	obtain	VERB
esrj-50337	196	109	with	with	ADP
esrj-50337	196	110	the	the	DET
esrj-50337	196	111	proposed	propose	VERB
esrj-50337	196	112	method	method	NOUN
esrj-50337	196	113	are	be	AUX
esrj-50337	196	114	believed	believe	VERB
esrj-50337	196	115	to	to	PART
esrj-50337	196	116	be	be	AUX
esrj-50337	196	117	sufficiently	sufficiently	ADV
esrj-50337	196	118	accurate	accurate	ADJ
esrj-50337	196	119	for	for	ADP
esrj-50337	196	120	engineering	engineering	NOUN
esrj-50337	196	121	calculations	calculation	NOUN
esrj-50337	196	122	,	,	PUNCT
esrj-50337	196	123	among	among	ADP
esrj-50337	196	124	other	other	ADJ
esrj-50337	196	125	uses	use	NOUN
esrj-50337	196	126	.	.	PUNCT
esrj-50337	197	1	acknowledgements	acknowledgement	NOUN
esrj-50337	197	2	the	the	DET
esrj-50337	197	3	authors	author	NOUN
esrj-50337	197	4	thank	thank	VERB
esrj-50337	197	5	the	the	DET
esrj-50337	197	6	direction	direction	NOUN
esrj-50337	197	7	of	of	ADP
esrj-50337	197	8	research	research	NOUN
esrj-50337	197	9	and	and	CCONJ
esrj-50337	197	10	development	development	NOUN
esrj-50337	197	11	of	of	ADP
esrj-50337	197	12	the	the	DET
esrj-50337	197	13	university	university	NOUN
esrj-50337	197	14	of	of	ADP
esrj-50337	197	15	la	la	DET
esrj-50337	197	16	serena	serena	PROPN
esrj-50337	197	17	(	(	PUNCT
esrj-50337	197	18	diduls	diduls	PROPN
esrj-50337	197	19	)	)	PUNCT
esrj-50337	197	20	through	through	ADP
esrj-50337	197	21	the	the	DET
esrj-50337	197	22	research	research	NOUN
esrj-50337	197	23	project	project	NOUN
esrj-50337	197	24	peq16141	peq16141	NOUN
esrj-50337	197	25	,	,	PUNCT
esrj-50337	197	26	and	and	CCONJ
esrj-50337	197	27	the	the	DET
esrj-50337	197	28	department	department	NOUN
esrj-50337	197	29	of	of	ADP
esrj-50337	197	30	physics	physics	PROPN
esrj-50337	197	31	of	of	ADP
esrj-50337	197	32	the	the	DET
esrj-50337	197	33	university	university	NOUN
esrj-50337	197	34	of	of	ADP
esrj-50337	197	35	la	la	DET
esrj-50337	197	36	serena	serena	PROPN
esrj-50337	197	37	(	(	PUNCT
esrj-50337	197	38	dfuls	dfuls	PROPN
esrj-50337	197	39	)	)	PUNCT
esrj-50337	197	40	for	for	ADP
esrj-50337	197	41	the	the	DET
esrj-50337	197	42	special	special	ADJ
esrj-50337	197	43	support	support	NOUN
esrj-50337	197	44	that	that	PRON
esrj-50337	197	45	made	make	VERB
esrj-50337	197	46	possible	possible	ADJ
esrj-50337	197	47	the	the	DET
esrj-50337	197	48	preparation	preparation	NOUN
esrj-50337	197	49	of	of	ADP
esrj-50337	197	50	this	this	DET
esrj-50337	197	51	paper	paper	NOUN
esrj-50337	197	52	.	.	PUNCT
esrj-50337	198	1	special	special	ADJ
esrj-50337	198	2	acknowledgements	acknowledgement	NOUN
esrj-50337	198	3	to	to	ADP
esrj-50337	198	4	dr	dr	PROPN
esrj-50337	198	5	.	.	PROPN
esrj-50337	198	6	pedro	pedro	PROPN
esrj-50337	198	7	vega	vega	PROPN
esrj-50337	198	8	(	(	PUNCT
esrj-50337	198	9	uls	uls	PROPN
esrj-50337	198	10	)	)	PUNCT
esrj-50337	198	11	for	for	ADP
esrj-50337	198	12	the	the	DET
esrj-50337	198	13	support	support	NOUN
esrj-50337	198	14	that	that	PRON
esrj-50337	198	15	made	make	VERB
esrj-50337	198	16	possible	possible	ADJ
esrj-50337	198	17	the	the	DET
esrj-50337	198	18	implementation	implementation	NOUN
esrj-50337	198	19	of	of	ADP
esrj-50337	198	20	the	the	DET
esrj-50337	198	21	meteorological	meteorological	ADJ
esrj-50337	198	22	station	station	NOUN
esrj-50337	198	23	and	and	CCONJ
esrj-50337	198	24	to	to	ADP
esrj-50337	198	25	mg	mg	PROPN
esrj-50337	198	26	.	.	PUNCT
esrj-50337	199	1	julio	julio	PROPN
esrj-50337	199	2	marín	marín	PROPN
esrj-50337	199	3	(	(	PUNCT
esrj-50337	199	4	uls	uls	PROPN
esrj-50337	199	5	)	)	PUNCT
esrj-50337	199	6	for	for	ADP
esrj-50337	199	7	providing	provide	VERB
esrj-50337	199	8	the	the	DET
esrj-50337	199	9	meteorological	meteorological	ADJ
esrj-50337	199	10	data	datum	NOUN
esrj-50337	199	11	set	set	VERB
esrj-50337	199	12	.	.	PUNCT
esrj-50337	200	1	references	reference	NOUN
esrj-50337	200	2	akdağ	akdağ	NOUN
esrj-50337	200	3	,	,	PUNCT
esrj-50337	200	4	s.a	s.a	PROPN
esrj-50337	200	5	.	.	PROPN
esrj-50337	200	6	and	and	CCONJ
esrj-50337	200	7	güler	güler	PROPN
esrj-50337	200	8	ö.	ö.	PROPN
esrj-50337	200	9	(	(	PUNCT
esrj-50337	200	10	2011	2011	NUM
esrj-50337	200	11	)	)	PUNCT
esrj-50337	200	12	.	.	PUNCT
esrj-50337	201	1	a	a	DET
esrj-50337	201	2	comparison	comparison	NOUN
esrj-50337	201	3	of	of	ADP
esrj-50337	201	4	wind	wind	NOUN
esrj-50337	201	5	turbine	turbine	NOUN
esrj-50337	201	6	power	power	NOUN
esrj-50337	201	7	curve	curve	NOUN
esrj-50337	201	8	models	model	NOUN
esrj-50337	201	9	.	.	PUNCT
esrj-50337	202	1	energy	energy	NOUN
esrj-50337	202	2	sources	source	NOUN
esrj-50337	202	3	part	part	VERB
esrj-50337	202	4	a	a	PRON
esrj-50337	202	5	,	,	PUNCT
esrj-50337	202	6	33	33	NUM
esrj-50337	202	7	,	,	PUNCT
esrj-50337	202	8	2257–2263	2257–2263	NUM
esrj-50337	202	9	.	.	PUNCT
esrj-50337	203	1	awad	awad	PROPN
esrj-50337	203	2	,	,	PUNCT
esrj-50337	203	3	m.	m.	NOUN
esrj-50337	203	4	,	,	PUNCT
esrj-50337	203	5	pomares	pomare	NOUN
esrj-50337	203	6	,	,	PUNCT
esrj-50337	203	7	h.	h.	PROPN
esrj-50337	203	8	,	,	PUNCT
esrj-50337	203	9	rojas	rojas	PROPN
esrj-50337	203	10	,	,	PUNCT
esrj-50337	203	11	i.	i.	PROPN
esrj-50337	203	12	,	,	PUNCT
esrj-50337	203	13	salameh	salameh	NOUN
esrj-50337	203	14	,	,	PUNCT
esrj-50337	203	15	o.	o.	PROPN
esrj-50337	203	16	and	and	CCONJ
esrj-50337	203	17	hamdon	hamdon	NOUN
esrj-50337	203	18	,	,	PUNCT
esrj-50337	203	19	m.	m.	NOUN
esrj-50337	203	20	(	(	PUNCT
esrj-50337	203	21	2009	2009	NUM
esrj-50337	203	22	)	)	PUNCT
esrj-50337	203	23	.	.	PUNCT
esrj-50337	204	1	prediction	prediction	NOUN
esrj-50337	204	2	of	of	ADP
esrj-50337	204	3	time	time	NOUN
esrj-50337	204	4	series	series	PROPN
esrj-50337	204	5	using	use	VERB
esrj-50337	204	6	rbf	rbf	PROPN
esrj-50337	204	7	neural	neural	ADJ
esrj-50337	204	8	networks	network	NOUN
esrj-50337	204	9	:	:	PUNCT
esrj-50337	204	10	a	a	DET
esrj-50337	204	11	new	new	ADJ
esrj-50337	204	12	approach	approach	NOUN
esrj-50337	204	13	of	of	ADP
esrj-50337	204	14	clustering	clustering	NOUN
esrj-50337	204	15	.	.	PUNCT
esrj-50337	205	1	international	international	ADJ
esrj-50337	205	2	arab	arab	PROPN
esrj-50337	205	3	journal	journal	PROPN
esrj-50337	205	4	of	of	ADP
esrj-50337	205	5	information	information	NOUN
esrj-50337	205	6	technology	technology	NOUN
esrj-50337	205	7	,	,	PUNCT
esrj-50337	205	8	6	6	NUM
esrj-50337	205	9	,	,	PUNCT
esrj-50337	205	10	138–143	138–143	NUM
esrj-50337	205	11	.	.	PUNCT
esrj-50337	206	1	bersini	bersini	PROPN
esrj-50337	206	2	,	,	PUNCT
esrj-50337	206	3	h.	h.	PROPN
esrj-50337	206	4	,	,	PUNCT
esrj-50337	206	5	duchateau	duchateau	NOUN
esrj-50337	206	6	,	,	PUNCT
esrj-50337	206	7	a.	a.	NOUN
esrj-50337	206	8	and	and	CCONJ
esrj-50337	206	9	bradshaw	bradshaw	PROPN
esrj-50337	206	10	,	,	PUNCT
esrj-50337	206	11	n.	n.	NOUN
esrj-50337	206	12	(	(	PUNCT
esrj-50337	206	13	1997	1997	NUM
esrj-50337	206	14	)	)	PUNCT
esrj-50337	206	15	.	.	PUNCT
esrj-50337	207	1	using	use	VERB
esrj-50337	207	2	incremental	incremental	ADJ
esrj-50337	207	3	learning	learning	NOUN
esrj-50337	207	4	algorithms	algorithm	NOUN
esrj-50337	207	5	in	in	ADP
esrj-50337	207	6	the	the	DET
esrj-50337	207	7	search	search	NOUN
esrj-50337	207	8	for	for	ADP
esrj-50337	207	9	minimal	minimal	ADJ
esrj-50337	207	10	effective	effective	ADJ
esrj-50337	207	11	fuzzy	fuzzy	ADJ
esrj-50337	207	12	models	model	NOUN
esrj-50337	207	13	.	.	PUNCT
esrj-50337	208	1	in	in	ADP
esrj-50337	208	2	:	:	PUNCT
esrj-50337	208	3	proceedings	proceeding	NOUN
esrj-50337	208	4	of	of	ADP
esrj-50337	208	5	6th	6th	ADJ
esrj-50337	208	6	international	international	ADJ
esrj-50337	208	7	conference	conference	NOUN
esrj-50337	208	8	on	on	ADP
esrj-50337	208	9	fuzzy	fuzzy	ADJ
esrj-50337	208	10	systems	system	NOUN
esrj-50337	208	11	,	,	PUNCT
esrj-50337	208	12	pp	pp	X
esrj-50337	208	13	.	.	PUNCT
esrj-50337	208	14	1417–1422	1417–1422	NUM
esrj-50337	208	15	.	.	PUNCT
esrj-50337	209	1	bezruchko	bezruchko	PROPN
esrj-50337	209	2	,	,	PUNCT
esrj-50337	209	3	b.p	b.p	PROPN
esrj-50337	209	4	.	.	PROPN
esrj-50337	209	5	,	,	PUNCT
esrj-50337	209	6	karavaev	karavaev	PROPN
esrj-50337	209	7	,	,	PUNCT
esrj-50337	209	8	a.s	a.s	PROPN
esrj-50337	209	9	.	.	PROPN
esrj-50337	209	10	,	,	PUNCT
esrj-50337	209	11	ponomarenko	ponomarenko	PROPN
esrj-50337	209	12	,	,	PUNCT
esrj-50337	209	13	v.i	v.i	PROPN
esrj-50337	209	14	.	.	PROPN
esrj-50337	209	15	and	and	CCONJ
esrj-50337	209	16	prokhorov	prokhorov	PROPN
esrj-50337	209	17	,	,	PUNCT
esrj-50337	209	18	m.d	m.d	PROPN
esrj-50337	209	19	.	.	PROPN
esrj-50337	209	20	(	(	PUNCT
esrj-50337	209	21	2001	2001	NUM
esrj-50337	209	22	)	)	PUNCT
esrj-50337	209	23	.	.	PUNCT
esrj-50337	210	1	reconstruction	reconstruction	NOUN
esrj-50337	210	2	of	of	ADP
esrj-50337	210	3	time	time	NOUN
esrj-50337	210	4	-	-	PUNCT
esrj-50337	210	5	delay	delay	NOUN
esrj-50337	210	6	systems	system	NOUN
esrj-50337	210	7	from	from	ADP
esrj-50337	210	8	chaotic	chaotic	ADJ
esrj-50337	210	9	time	time	NOUN
esrj-50337	210	10	series	series	NOUN
esrj-50337	210	11	.	.	PUNCT
esrj-50337	211	1	physical	physical	ADJ
esrj-50337	211	2	review	review	PROPN
esrj-50337	211	3	e	e	NOUN
esrj-50337	211	4	,	,	PUNCT
esrj-50337	211	5	64	64	NUM
esrj-50337	211	6	,	,	PUNCT
esrj-50337	211	7	056216	056216	NUM
esrj-50337	211	8	.	.	PUNCT
esrj-50337	212	1	çam	çam	PROPN
esrj-50337	212	2	,	,	PUNCT
esrj-50337	212	3	e.	e.	PROPN
esrj-50337	212	4	and	and	CCONJ
esrj-50337	212	5	yildiz	yildiz	PROPN
esrj-50337	212	6	,	,	PUNCT
esrj-50337	212	7	o.	o.	PROPN
esrj-50337	212	8	(	(	PUNCT
esrj-50337	212	9	2006	2006	NUM
esrj-50337	212	10	)	)	PUNCT
esrj-50337	212	11	.	.	PUNCT
esrj-50337	213	1	prediction	prediction	NOUN
esrj-50337	213	2	of	of	ADP
esrj-50337	213	3	wind	wind	NOUN
esrj-50337	213	4	speed	speed	NOUN
esrj-50337	213	5	and	and	CCONJ
esrj-50337	213	6	power	power	NOUN
esrj-50337	213	7	in	in	ADP
esrj-50337	213	8	the	the	DET
esrj-50337	213	9	central	central	ADJ
esrj-50337	213	10	anatolian	anatolian	ADJ
esrj-50337	213	11	region	region	NOUN
esrj-50337	213	12	of	of	ADP
esrj-50337	213	13	turkey	turkey	NOUN
esrj-50337	213	14	by	by	ADP
esrj-50337	213	15	adaptative	adaptative	ADJ
esrj-50337	213	16	neuro	neuro	NOUN
esrj-50337	213	17	-	-	PUNCT
esrj-50337	213	18	fuzzy	fuzzy	ADJ
esrj-50337	213	19	inference	inference	NOUN
esrj-50337	213	20	systems	system	NOUN
esrj-50337	213	21	(	(	PUNCT
esrj-50337	213	22	anfis	anfis	PROPN
esrj-50337	213	23	)	)	PUNCT
esrj-50337	213	24	.	.	PUNCT
esrj-50337	214	1	turkish	turkish	ADJ
esrj-50337	214	2	journal	journal	NOUN
esrj-50337	214	3	of	of	ADP
esrj-50337	214	4	engineering	engineering	NOUN
esrj-50337	214	5	and	and	CCONJ
esrj-50337	214	6	environmental	environmental	ADJ
esrj-50337	214	7	sciences	science	NOUN
esrj-50337	214	8	,	,	PUNCT
esrj-50337	214	9	30	30	NUM
esrj-50337	214	10	,	,	PUNCT
esrj-50337	214	11	35–41	35–41	NUM
esrj-50337	214	12	.	.	PUNCT
esrj-50337	215	1	chng	chng	PROPN
esrj-50337	215	2	,	,	PUNCT
esrj-50337	215	3	e.s	e.s	PROPN
esrj-50337	215	4	.	.	PROPN
esrj-50337	215	5	,	,	PUNCT
esrj-50337	215	6	chen	chen	PROPN
esrj-50337	215	7	,	,	PUNCT
esrj-50337	215	8	s.	s.	PROPN
esrj-50337	215	9	and	and	CCONJ
esrj-50337	215	10	mulgrew	mulgrew	PROPN
esrj-50337	215	11	,	,	PUNCT
esrj-50337	215	12	b.	b.	PROPN
esrj-50337	215	13	(	(	PUNCT
esrj-50337	215	14	1996	1996	NUM
esrj-50337	215	15	)	)	PUNCT
esrj-50337	215	16	.	.	PUNCT
esrj-50337	216	1	gradient	gradient	ADJ
esrj-50337	216	2	radial	radial	ADJ
esrj-50337	216	3	basis	basis	NOUN
esrj-50337	216	4	function	function	NOUN
esrj-50337	216	5	networks	network	NOUN
esrj-50337	216	6	for	for	ADP
esrj-50337	216	7	nonlinear	nonlinear	ADJ
esrj-50337	216	8	and	and	CCONJ
esrj-50337	216	9	nonstationary	nonstationary	ADJ
esrj-50337	216	10	time	time	NOUN
esrj-50337	216	11	series	series	PROPN
esrj-50337	216	12	prediction	prediction	PROPN
esrj-50337	216	13	.	.	PUNCT
esrj-50337	217	1	ieee	ieee	NOUN
esrj-50337	217	2	transactions	transaction	NOUN
esrj-50337	217	3	on	on	ADP
esrj-50337	217	4	neural	neural	ADJ
esrj-50337	217	5	networks	network	NOUN
esrj-50337	217	6	,	,	PUNCT
esrj-50337	217	7	7	7	NUM
esrj-50337	217	8	,	,	PUNCT
esrj-50337	217	9	190–194	190–194	NUM
esrj-50337	217	10	.	.	PUNCT
esrj-50337	218	1	chua	chua	PROPN
esrj-50337	218	2	,	,	PUNCT
esrj-50337	218	3	l.o	l.o	PROPN
esrj-50337	218	4	.	.	PROPN
esrj-50337	218	5	,	,	PUNCT
esrj-50337	218	6	kocarev	kocarev	PROPN
esrj-50337	218	7	,	,	PUNCT
esrj-50337	218	8	l.	l.	PROPN
esrj-50337	218	9	,	,	PUNCT
esrj-50337	218	10	eckert	eckert	PROPN
esrj-50337	218	11	,	,	PUNCT
esrj-50337	218	12	k.	k.	PROPN
esrj-50337	218	13	and	and	CCONJ
esrj-50337	218	14	itoh	itoh	PROPN
esrj-50337	218	15	,	,	PUNCT
esrj-50337	218	16	m.	m.	NOUN
esrj-50337	218	17	(	(	PUNCT
esrj-50337	218	18	1992	1992	NUM
esrj-50337	218	19	)	)	PUNCT
esrj-50337	218	20	.	.	PUNCT
esrj-50337	219	1	experimental	experimental	ADJ
esrj-50337	219	2	chaos	chaos	NOUN
esrj-50337	219	3	synchronization	synchronization	NOUN
esrj-50337	219	4	in	in	ADP
esrj-50337	219	5	chua	chua	PROPN
esrj-50337	219	6	’s	’s	PART
esrj-50337	219	7	circuit	circuit	NOUN
esrj-50337	219	8	.	.	PUNCT
esrj-50337	220	1	international	international	ADJ
esrj-50337	220	2	journal	journal	PROPN
esrj-50337	220	3	of	of	ADP
esrj-50337	220	4	bifurcation	bifurcation	NOUN
esrj-50337	220	5	and	and	CCONJ
esrj-50337	220	6	chaos	chaos	NOUN
esrj-50337	220	7	,	,	PUNCT
esrj-50337	220	8	2	2	NUM
esrj-50337	220	9	,	,	PUNCT
esrj-50337	220	10	705–708	705–708	NUM
esrj-50337	220	11	.	.	PUNCT
esrj-50337	221	1	eberhart	eberhart	PROPN
esrj-50337	221	2	,	,	PUNCT
esrj-50337	221	3	r.c	r.c	PROPN
esrj-50337	221	4	.	.	PROPN
esrj-50337	221	5	and	and	CCONJ
esrj-50337	221	6	kennedy	kennedy	PROPN
esrj-50337	221	7	,	,	PUNCT
esrj-50337	221	8	j.	j.	PROPN
esrj-50337	221	9	(	(	PUNCT
esrj-50337	221	10	1995	1995	NUM
esrj-50337	221	11	)	)	PUNCT
esrj-50337	221	12	.	.	PUNCT
esrj-50337	222	1	a	a	DET
esrj-50337	222	2	new	new	ADJ
esrj-50337	222	3	optimizer	optimizer	NOUN
esrj-50337	222	4	using	use	VERB
esrj-50337	222	5	particle	particle	NOUN
esrj-50337	222	6	swarm	swarm	NOUN
esrj-50337	222	7	theory	theory	NOUN
esrj-50337	222	8	.	.	PUNCT
esrj-50337	223	1	in	in	ADP
esrj-50337	223	2	:	:	PUNCT
esrj-50337	223	3	proceedings	proceeding	NOUN
esrj-50337	223	4	of	of	ADP
esrj-50337	223	5	6th	6th	ADJ
esrj-50337	223	6	international	international	ADJ
esrj-50337	223	7	symposium	symposium	NOUN
esrj-50337	223	8	on	on	ADP
esrj-50337	223	9	micro	micro	ADJ
esrj-50337	223	10	machine	machine	NOUN
esrj-50337	223	11	and	and	CCONJ
esrj-50337	223	12	human	human	ADJ
esrj-50337	223	13	science	science	NOUN
esrj-50337	223	14	,	,	PUNCT
esrj-50337	223	15	nagoya	nagoya	PROPN
esrj-50337	223	16	.	.	PUNCT
esrj-50337	223	17	new	new	PROPN
esrj-50337	223	18	york	york	PROPN
esrj-50337	223	19	,	,	PUNCT
esrj-50337	223	20	ny	ny	PROPN
esrj-50337	223	21	,	,	PUNCT
esrj-50337	223	22	usa	usa	PROPN
esrj-50337	223	23	:	:	PUNCT
esrj-50337	223	24	ieee	ieee	NOUN
esrj-50337	223	25	,	,	PUNCT
esrj-50337	223	26	pp	pp	ADJ
esrj-50337	223	27	.	.	PUNCT
esrj-50337	224	1	39–43	39–43	NUM
esrj-50337	224	2	.	.	PUNCT
esrj-50337	225	1	farmer	farmer	PROPN
esrj-50337	225	2	,	,	PUNCT
esrj-50337	225	3	j.d	j.d	PROPN
esrj-50337	225	4	.	.	PROPN
esrj-50337	225	5	(	(	PUNCT
esrj-50337	225	6	1982	1982	NUM
esrj-50337	225	7	)	)	PUNCT
esrj-50337	225	8	.	.	PUNCT
esrj-50337	226	1	chaotic	chaotic	ADJ
esrj-50337	226	2	attractors	attractor	NOUN
esrj-50337	226	3	of	of	ADP
esrj-50337	226	4	an	an	DET
esrj-50337	226	5	infinite	infinite	ADJ
esrj-50337	226	6	-	-	PUNCT
esrj-50337	226	7	dimensional	dimensional	ADJ
esrj-50337	226	8	dynamical	dynamical	ADJ
esrj-50337	226	9	system	system	NOUN
esrj-50337	226	10	.	.	PUNCT
esrj-50337	227	1	physica	physica	PROPN
esrj-50337	227	2	d	d	PROPN
esrj-50337	227	3	,	,	PUNCT
esrj-50337	227	4	4	4	NUM
esrj-50337	227	5	,	,	PUNCT
esrj-50337	227	6	366–393	366–393	NUM
esrj-50337	227	7	.	.	PUNCT
esrj-50337	228	1	freeman	freeman	PROPN
esrj-50337	228	2	,	,	PUNCT
esrj-50337	228	3	j.a	j.a	PROPN
esrj-50337	228	4	.	.	PROPN
esrj-50337	228	5	and	and	CCONJ
esrj-50337	228	6	skapura	skapura	NOUN
esrj-50337	228	7	,	,	PUNCT
esrj-50337	228	8	d.m	d.m	PROPN
esrj-50337	228	9	.	.	PUNCT
esrj-50337	228	10	(	(	PUNCT
esrj-50337	228	11	1991	1991	NUM
esrj-50337	228	12	)	)	PUNCT
esrj-50337	228	13	.	.	PUNCT
esrj-50337	229	1	neural	neural	ADJ
esrj-50337	229	2	networks	network	NOUN
esrj-50337	229	3	:	:	PUNCT
esrj-50337	229	4	algorithms	algorithm	NOUN
esrj-50337	229	5	,	,	PUNCT
esrj-50337	229	6	applications	application	NOUN
esrj-50337	229	7	and	and	CCONJ
esrj-50337	229	8	programming	programming	NOUN
esrj-50337	229	9	techniques	technique	NOUN
esrj-50337	229	10	.	.	PUNCT
esrj-50337	230	1	computation	computation	NOUN
esrj-50337	230	2	and	and	CCONJ
esrj-50337	230	3	neural	neural	ADJ
esrj-50337	230	4	systems	system	NOUN
esrj-50337	230	5	series	series	PROPN
esrj-50337	230	6	.	.	PUNCT
esrj-50337	231	1	massachusetts	massachusetts	PROPN
esrj-50337	231	2	,	,	PUNCT
esrj-50337	231	3	usa	usa	PROPN
esrj-50337	231	4	:	:	PUNCT
esrj-50337	231	5	addison	addison	PROPN
esrj-50337	231	6	-	-	PUNCT
esrj-50337	231	7	wesley	wesley	PROPN
esrj-50337	231	8	.	.	PUNCT
esrj-50337	232	1	hagan	hagan	PROPN
esrj-50337	232	2	,	,	PUNCT
esrj-50337	232	3	m.t	m.t	PROPN
esrj-50337	232	4	.	.	PROPN
esrj-50337	232	5	and	and	CCONJ
esrj-50337	232	6	menhaj	menhaj	PROPN
esrj-50337	232	7	,	,	PUNCT
esrj-50337	232	8	m.b	m.b	PROPN
esrj-50337	232	9	.	.	PROPN
esrj-50337	232	10	(	(	PUNCT
esrj-50337	232	11	1994	1994	NUM
esrj-50337	232	12	)	)	PUNCT
esrj-50337	232	13	.	.	PUNCT
esrj-50337	233	1	training	train	VERB
esrj-50337	233	2	feedforward	feedforward	NOUN
esrj-50337	233	3	networks	network	NOUN
esrj-50337	233	4	with	with	ADP
esrj-50337	233	5	the	the	DET
esrj-50337	233	6	marquardt	marquardt	PROPN
esrj-50337	233	7	algorithm	algorithm	PROPN
esrj-50337	233	8	,	,	PUNCT
esrj-50337	233	9	ieee	ieee	NOUN
esrj-50337	233	10	transactions	transaction	NOUN
esrj-50337	233	11	on	on	ADP
esrj-50337	233	12	neural	neural	ADJ
esrj-50337	233	13	networks	network	NOUN
esrj-50337	233	14	,	,	PUNCT
esrj-50337	233	15	5	5	NUM
esrj-50337	233	16	,	,	PUNCT
esrj-50337	233	17	989–993	989–993	NUM
esrj-50337	233	18	.	.	PUNCT
esrj-50337	234	1	han	han	PROPN
esrj-50337	234	2	,	,	PUNCT
esrj-50337	234	3	m.	m.	NOUN
esrj-50337	234	4	and	and	CCONJ
esrj-50337	234	5	wang	wang	PROPN
esrj-50337	234	6	,	,	PUNCT
esrj-50337	234	7	y.	y.	PROPN
esrj-50337	234	8	(	(	PUNCT
esrj-50337	234	9	2009	2009	NUM
esrj-50337	234	10	)	)	PUNCT
esrj-50337	234	11	.	.	PUNCT
esrj-50337	235	1	analysis	analysis	NOUN
esrj-50337	235	2	and	and	CCONJ
esrj-50337	235	3	modeling	modeling	NOUN
esrj-50337	235	4	of	of	ADP
esrj-50337	235	5	multivariate	multivariate	NOUN
esrj-50337	235	6	chaotic	chaotic	ADJ
esrj-50337	235	7	time	time	NOUN
esrj-50337	235	8	series	series	NOUN
esrj-50337	235	9	based	base	VERB
esrj-50337	235	10	on	on	ADP
esrj-50337	235	11	neural	neural	ADJ
esrj-50337	235	12	network	network	NOUN
esrj-50337	235	13	.	.	PUNCT
esrj-50337	236	1	expert	expert	NOUN
esrj-50337	236	2	systems	system	NOUN
esrj-50337	236	3	with	with	ADP
esrj-50337	236	4	applications	application	NOUN
esrj-50337	236	5	,	,	PUNCT
esrj-50337	236	6	36	36	NUM
esrj-50337	236	7	,	,	PUNCT
esrj-50337	236	8	1280–1290	1280–1290	NUM
esrj-50337	236	9	.	.	PUNCT
esrj-50337	237	1	hanağasioğlu	hanağasioğlu	PROPN
esrj-50337	237	2	,	,	PUNCT
esrj-50337	237	3	m.	m.	NOUN
esrj-50337	237	4	(	(	PUNCT
esrj-50337	237	5	1999	1999	NUM
esrj-50337	237	6	)	)	PUNCT
esrj-50337	237	7	.	.	PUNCT
esrj-50337	238	1	wind	wind	NOUN
esrj-50337	238	2	energy	energy	NOUN
esrj-50337	238	3	in	in	ADP
esrj-50337	238	4	turkey	turkey	NOUN
esrj-50337	238	5	.	.	PUNCT
esrj-50337	239	1	renewable	renewable	ADJ
esrj-50337	239	2	energy	energy	NOUN
esrj-50337	239	3	,	,	PUNCT
esrj-50337	239	4	16	16	NUM
esrj-50337	239	5	,	,	PUNCT
esrj-50337	239	6	822–827	822–827	NUM
esrj-50337	239	7	.	.	PUNCT
esrj-50337	240	1	ikeda	ikeda	PROPN
esrj-50337	240	2	,	,	PUNCT
esrj-50337	240	3	k.	k.	PROPN
esrj-50337	240	4	(	(	PUNCT
esrj-50337	240	5	1979	1979	NUM
esrj-50337	240	6	)	)	PUNCT
esrj-50337	240	7	.	.	PUNCT
esrj-50337	241	1	multiple	multiple	ADV
esrj-50337	241	2	-	-	PUNCT
esrj-50337	241	3	valued	value	VERB
esrj-50337	241	4	stationary	stationary	ADJ
esrj-50337	241	5	state	state	NOUN
esrj-50337	241	6	and	and	CCONJ
esrj-50337	241	7	its	its	PRON
esrj-50337	241	8	instability	instability	NOUN
esrj-50337	241	9	of	of	ADP
esrj-50337	241	10	the	the	DET
esrj-50337	241	11	transmitted	transmit	VERB
esrj-50337	241	12	light	light	NOUN
esrj-50337	241	13	by	by	ADP
esrj-50337	241	14	a	a	DET
esrj-50337	241	15	ring	ring	NOUN
esrj-50337	241	16	cavity	cavity	NOUN
esrj-50337	241	17	system	system	NOUN
esrj-50337	241	18	.	.	PUNCT
esrj-50337	242	1	optics	optic	NOUN
esrj-50337	242	2	communications	communication	NOUN
esrj-50337	242	3	,	,	PUNCT
esrj-50337	242	4	30	30	NUM
esrj-50337	242	5	,	,	PUNCT
esrj-50337	242	6	257–261	257–261	NUM
esrj-50337	242	7	.	.	PUNCT
esrj-50337	243	1	kallos	kallo	NOUN
esrj-50337	243	2	,	,	PUNCT
esrj-50337	243	3	g.	g.	PROPN
esrj-50337	243	4	,	,	PUNCT
esrj-50337	243	5	galanis	galanis	PROPN
esrj-50337	243	6	,	,	PUNCT
esrj-50337	243	7	g.	g.	PROPN
esrj-50337	243	8	and	and	CCONJ
esrj-50337	243	9	katsafados	katsafado	NOUN
esrj-50337	243	10	,	,	PUNCT
esrj-50337	243	11	p.	p.	NOUN
esrj-50337	243	12	(	(	PUNCT
esrj-50337	243	13	2007	2007	NUM
esrj-50337	243	14	)	)	PUNCT
esrj-50337	243	15	.	.	PUNCT
esrj-50337	244	1	local	local	ADJ
esrj-50337	244	2	wind	wind	NOUN
esrj-50337	244	3	speed	speed	NOUN
esrj-50337	244	4	forecasting	forecasting	NOUN
esrj-50337	244	5	and	and	CCONJ
esrj-50337	244	6	applications	application	NOUN
esrj-50337	244	7	to	to	ADP
esrj-50337	244	8	power	power	NOUN
esrj-50337	244	9	prediction	prediction	NOUN
esrj-50337	244	10	.	.	PUNCT
esrj-50337	245	1	geophysical	geophysical	ADJ
esrj-50337	245	2	research	research	NOUN
esrj-50337	245	3	,	,	PUNCT
esrj-50337	245	4	9	9	NUM
esrj-50337	245	5	,	,	PUNCT
esrj-50337	245	6	93–99	93–99	NUM
esrj-50337	245	7	.	.	PUNCT
esrj-50337	246	1	kalthoff	kalthoff	NOUN
esrj-50337	246	2	,	,	PUNCT
esrj-50337	246	3	n.	n.	PROPN
esrj-50337	246	4	,	,	PUNCT
esrj-50337	246	5	bischoff	bischoff	PROPN
esrj-50337	246	6	-	-	PUNCT
esrj-50337	246	7	gauß	gauß	PROPN
esrj-50337	246	8	,	,	PUNCT
esrj-50337	246	9	i.	i.	NOUN
esrj-50337	246	10	,	,	PUNCT
esrj-50337	246	11	fiebig	fiebig	ADJ
esrj-50337	246	12	-	-	PUNCT
esrj-50337	246	13	wittmaack	wittmaack	NOUN
esrj-50337	246	14	,	,	PUNCT
esrj-50337	246	15	m.	m.	NOUN
esrj-50337	246	16	,	,	PUNCT
esrj-50337	246	17	fiedler	fiedler	PROPN
esrj-50337	246	18	,	,	PUNCT
esrj-50337	246	19	f.	f.	PROPN
esrj-50337	246	20	,	,	PUNCT
esrj-50337	246	21	thürauf	thürauf	PROPN
esrj-50337	246	22	,	,	PUNCT
esrj-50337	246	23	j.	j.	PROPN
esrj-50337	246	24	,	,	PUNCT
esrj-50337	246	25	novoa	novoa	NOUN
esrj-50337	246	26	,	,	PUNCT
esrj-50337	246	27	e.	e.	PROPN
esrj-50337	246	28	,	,	PUNCT
esrj-50337	246	29	pizarro	pizarro	PROPN
esrj-50337	246	30	,	,	PUNCT
esrj-50337	246	31	c.	c.	PROPN
esrj-50337	246	32	,	,	PUNCT
esrj-50337	246	33	castillo	castillo	PROPN
esrj-50337	246	34	,	,	PUNCT
esrj-50337	246	35	r.	r.	PROPN
esrj-50337	246	36	,	,	PUNCT
esrj-50337	246	37	gallardo	gallardo	PROPN
esrj-50337	246	38	,	,	PUNCT
esrj-50337	246	39	l.	l.	PROPN
esrj-50337	246	40	,	,	PUNCT
esrj-50337	246	41	rondanelli	rondanelli	PROPN
esrj-50337	246	42	,	,	PUNCT
esrj-50337	246	43	r.	r.	PROPN
esrj-50337	246	44	and	and	CCONJ
esrj-50337	246	45	kohler	kohler	NOUN
esrj-50337	246	46	,	,	PUNCT
esrj-50337	246	47	m.	m.	NOUN
esrj-50337	246	48	(	(	PUNCT
esrj-50337	246	49	2002	2002	NUM
esrj-50337	246	50	)	)	PUNCT
esrj-50337	246	51	.	.	PUNCT
esrj-50337	247	1	mesoscale	mesoscale	ADJ
esrj-50337	247	2	wind	wind	NOUN
esrj-50337	247	3	regimes	regime	NOUN
esrj-50337	247	4	in	in	ADP
esrj-50337	247	5	chile	chile	NOUN
esrj-50337	247	6	at	at	ADP
esrj-50337	247	7	30º	30º	PROPN
esrj-50337	247	8	s.	s.	PROPN
esrj-50337	247	9	journal	journal	PROPN
esrj-50337	247	10	of	of	ADP
esrj-50337	247	11	applied	apply	VERB
esrj-50337	247	12	meteorology	meteorology	NOUN
esrj-50337	247	13	,	,	PUNCT
esrj-50337	247	14	41	41	NUM
esrj-50337	247	15	,	,	PUNCT
esrj-50337	247	16	953–970	953–970	NUM
esrj-50337	247	17	.	.	PUNCT
esrj-50337	248	1	kalthoff	kalthoff	PROPN
esrj-50337	248	2	,	,	PUNCT
esrj-50337	248	3	n.	n.	NOUN
esrj-50337	248	4	,	,	PUNCT
esrj-50337	248	5	fiebig	fiebig	ADJ
esrj-50337	248	6	-	-	PUNCT
esrj-50337	248	7	wittmaack	wittmaack	NOUN
esrj-50337	248	8	,	,	PUNCT
esrj-50337	248	9	m.	m.	NOUN
esrj-50337	248	10	,	,	PUNCT
esrj-50337	248	11	meißner	meißner	NOUN
esrj-50337	248	12	,	,	PUNCT
esrj-50337	248	13	c.	c.	NOUN
esrj-50337	248	14	,	,	PUNCT
esrj-50337	248	15	kohler	kohler	NOUN
esrj-50337	248	16	,	,	PUNCT
esrj-50337	248	17	m.	m.	NOUN
esrj-50337	248	18	,	,	PUNCT
esrj-50337	248	19	uriarte	uriarte	NOUN
esrj-50337	248	20	,	,	PUNCT
esrj-50337	248	21	m.	m.	NOUN
esrj-50337	248	22	and	and	CCONJ
esrj-50337	248	23	bischoff	bischoff	PROPN
esrj-50337	248	24	-	-	PUNCT
esrj-50337	248	25	gauß	gauß	PROPN
esrj-50337	248	26	,	,	PUNCT
esrj-50337	248	27	i.	i.	PROPN
esrj-50337	248	28	(	(	PUNCT
esrj-50337	248	29	2006	2006	NUM
esrj-50337	248	30	)	)	PUNCT
esrj-50337	248	31	.	.	PUNCT
esrj-50337	249	1	the	the	DET
esrj-50337	249	2	energy	energy	NOUN
esrj-50337	249	3	balance	balance	NOUN
esrj-50337	249	4	,	,	PUNCT
esrj-50337	249	5	evapotranspiration	evapotranspiration	NOUN
esrj-50337	249	6	and	and	CCONJ
esrj-50337	249	7	nocturnal	nocturnal	ADJ
esrj-50337	249	8	dew	dew	NOUN
esrj-50337	249	9	deposition	deposition	NOUN
esrj-50337	249	10	of	of	ADP
esrj-50337	249	11	an	an	DET
esrj-50337	249	12	arid	arid	ADJ
esrj-50337	249	13	valley	valley	NOUN
esrj-50337	249	14	in	in	ADP
esrj-50337	249	15	the	the	DET
esrj-50337	249	16	andes	andes	PROPN
esrj-50337	249	17	.	.	PROPN
esrj-50337	249	18	journal	journal	PROPN
esrj-50337	249	19	of	of	ADP
esrj-50337	249	20	arid	arid	ADJ
esrj-50337	249	21	environments	environment	NOUN
esrj-50337	249	22	,	,	PUNCT
esrj-50337	249	23	65	65	NUM
esrj-50337	249	24	,	,	PUNCT
esrj-50337	249	25	420–443	420–443	NUM
esrj-50337	249	26	.	.	PUNCT
esrj-50337	250	1	karunasinghe	karunasinghe	PROPN
esrj-50337	250	2	,	,	PUNCT
esrj-50337	250	3	d.s.k	d.s.k	NOUN
esrj-50337	250	4	.	.	PUNCT
esrj-50337	250	5	and	and	CCONJ
esrj-50337	250	6	liong	liong	NOUN
esrj-50337	250	7	,	,	PUNCT
esrj-50337	250	8	s.y	s.y	PROPN
esrj-50337	250	9	.	.	PROPN
esrj-50337	250	10	(	(	PUNCT
esrj-50337	250	11	2006	2006	NUM
esrj-50337	250	12	)	)	PUNCT
esrj-50337	250	13	.	.	PUNCT
esrj-50337	251	1	chaotic	chaotic	ADJ
esrj-50337	251	2	time	time	NOUN
esrj-50337	251	3	series	series	PROPN
esrj-50337	251	4	prediction	prediction	NOUN
esrj-50337	251	5	with	with	ADP
esrj-50337	251	6	a	a	DET
esrj-50337	251	7	global	global	ADJ
esrj-50337	251	8	model	model	NOUN
esrj-50337	251	9	:	:	PUNCT
esrj-50337	251	10	artificial	artificial	ADJ
esrj-50337	251	11	neural	neural	ADJ
esrj-50337	251	12	network	network	NOUN
esrj-50337	251	13	.	.	PUNCT
esrj-50337	252	1	journal	journal	PROPN
esrj-50337	252	2	of	of	ADP
esrj-50337	252	3	hydrology	hydrology	NOUN
esrj-50337	252	4	,	,	PUNCT
esrj-50337	252	5	323	323	NUM
esrj-50337	252	6	,	,	PUNCT
esrj-50337	252	7	92–105	92–105	PROPN
esrj-50337	252	8	.	.	PUNCT
esrj-50337	253	1	kennedy	kennedy	PROPN
esrj-50337	253	2	,	,	PUNCT
esrj-50337	253	3	j.	j.	PROPN
esrj-50337	253	4	,	,	PUNCT
esrj-50337	253	5	eberhart	eberhart	PROPN
esrj-50337	253	6	,	,	PUNCT
esrj-50337	253	7	r.c	r.c	PROPN
esrj-50337	253	8	.	.	PROPN
esrj-50337	253	9	and	and	CCONJ
esrj-50337	253	10	shi	shi	PROPN
esrj-50337	253	11	,	,	PUNCT
esrj-50337	253	12	y.	y.	PROPN
esrj-50337	253	13	(	(	PUNCT
esrj-50337	253	14	2001	2001	NUM
esrj-50337	253	15	)	)	PUNCT
esrj-50337	253	16	.	.	PUNCT
esrj-50337	254	1	swarm	swarm	NOUN
esrj-50337	254	2	intelligence	intelligence	NOUN
esrj-50337	254	3	.	.	PUNCT
esrj-50337	255	1	san	san	PROPN
esrj-50337	255	2	diego	diego	PROPN
esrj-50337	255	3	,	,	PUNCT
esrj-50337	255	4	ca	ca	PROPN
esrj-50337	255	5	,	,	PUNCT
esrj-50337	255	6	usa	usa	PROPN
esrj-50337	255	7	:	:	PUNCT
esrj-50337	255	8	academic	academic	ADJ
esrj-50337	255	9	press	press	NOUN
esrj-50337	255	10	.	.	PUNCT
esrj-50337	256	1	kulkarni	kulkarni	PROPN
esrj-50337	256	2	,	,	PUNCT
esrj-50337	256	3	m.a	m.a	PROPN
esrj-50337	256	4	.	.	PROPN
esrj-50337	256	5	,	,	PUNCT
esrj-50337	256	6	patil	patil	PROPN
esrj-50337	256	7	,	,	PUNCT
esrj-50337	256	8	s.	s.	PROPN
esrj-50337	256	9	,	,	PUNCT
esrj-50337	256	10	rama	rama	PROPN
esrj-50337	256	11	,	,	PUNCT
esrj-50337	256	12	g.v	g.v	PROPN
esrj-50337	256	13	.	.	PROPN
esrj-50337	256	14	and	and	CCONJ
esrj-50337	256	15	sen	sen	PROPN
esrj-50337	256	16	,	,	PUNCT
esrj-50337	256	17	p.n	p.n	PROPN
esrj-50337	256	18	.	.	PROPN
esrj-50337	256	19	(	(	PUNCT
esrj-50337	256	20	2008	2008	NUM
esrj-50337	256	21	)	)	PUNCT
esrj-50337	256	22	.	.	PUNCT
esrj-50337	257	1	wind	wind	NOUN
esrj-50337	257	2	speed	speed	NOUN
esrj-50337	257	3	prediction	prediction	NOUN
esrj-50337	257	4	using	use	VERB
esrj-50337	257	5	statistical	statistical	ADJ
esrj-50337	257	6	regression	regression	NOUN
esrj-50337	257	7	and	and	CCONJ
esrj-50337	257	8	neural	neural	ADJ
esrj-50337	257	9	nerwork	nerwork	NOUN
esrj-50337	257	10	.	.	PUNCT
esrj-50337	258	1	journal	journal	NOUN
esrj-50337	258	2	of	of	ADP
esrj-50337	258	3	earth	earth	NOUN
esrj-50337	258	4	system	system	NOUN
esrj-50337	258	5	science	science	NOUN
esrj-50337	258	6	,	,	PUNCT
esrj-50337	258	7	117	117	NUM
esrj-50337	258	8	,	,	PUNCT
esrj-50337	258	9	457–463	457–463	NUM
esrj-50337	258	10	.	.	PUNCT
esrj-50337	259	1	lazzús	lazzús	PROPN
esrj-50337	259	2	,	,	PUNCT
esrj-50337	259	3	j.a	j.a	PROPN
esrj-50337	259	4	.	.	PROPN
esrj-50337	259	5	(	(	PUNCT
esrj-50337	259	6	2011	2011	NUM
esrj-50337	259	7	)	)	PUNCT
esrj-50337	259	8	.	.	PUNCT
esrj-50337	260	1	predicting	predict	VERB
esrj-50337	260	2	natural	natural	ADJ
esrj-50337	260	3	and	and	CCONJ
esrj-50337	260	4	chaotic	chaotic	ADJ
esrj-50337	260	5	time	time	NOUN
esrj-50337	260	6	series	series	NOUN
esrj-50337	260	7	with	with	ADP
esrj-50337	260	8	a	a	DET
esrj-50337	260	9	swarmoptimized	swarmoptimize	VERB
esrj-50337	260	10	neural	neural	ADJ
esrj-50337	260	11	network	network	NOUN
esrj-50337	260	12	.	.	PUNCT
esrj-50337	261	1	chinese	chinese	ADJ
esrj-50337	261	2	physics	physics	NOUN
esrj-50337	261	3	letters	letter	NOUN
esrj-50337	261	4	,	,	PUNCT
esrj-50337	261	5	28	28	NUM
esrj-50337	261	6	,	,	PUNCT
esrj-50337	261	7	110504	110504	NUM
esrj-50337	261	8	.	.	PUNCT
esrj-50337	262	1	lazzús	lazzús	PROPN
esrj-50337	262	2	,	,	PUNCT
esrj-50337	262	3	j.a	j.a	PROPN
esrj-50337	262	4	.	.	PROPN
esrj-50337	262	5	(	(	PUNCT
esrj-50337	262	6	2013	2013	NUM
esrj-50337	262	7	)	)	PUNCT
esrj-50337	262	8	.	.	PUNCT
esrj-50337	263	1	neural	neural	ADJ
esrj-50337	263	2	network	network	NOUN
esrj-50337	263	3	-	-	PUNCT
esrj-50337	263	4	particle	particle	NOUN
esrj-50337	263	5	swarm	swarm	NOUN
esrj-50337	263	6	modeling	modeling	NOUN
esrj-50337	263	7	to	to	PART
esrj-50337	263	8	predict	predict	VERB
esrj-50337	263	9	thermal	thermal	ADJ
esrj-50337	263	10	properties	property	NOUN
esrj-50337	263	11	.	.	PUNCT
esrj-50337	264	1	mathematical	mathematical	ADJ
esrj-50337	264	2	and	and	CCONJ
esrj-50337	264	3	computer	computer	NOUN
esrj-50337	264	4	modelling	modelling	NOUN
esrj-50337	264	5	,	,	PUNCT
esrj-50337	264	6	57	57	NUM
esrj-50337	264	7	,	,	PUNCT
esrj-50337	264	8	2408–2418	2408–2418	NUM
esrj-50337	264	9	.	.	PUNCT
esrj-50337	265	1	lazzús	lazzús	PROPN
esrj-50337	265	2	,	,	PUNCT
esrj-50337	265	3	j.a	j.a	PROPN
esrj-50337	265	4	.	.	PROPN
esrj-50337	265	5	,	,	PUNCT
esrj-50337	265	6	salfate	salfate	VERB
esrj-50337	265	7	,	,	PUNCT
esrj-50337	265	8	i.	i.	NOUN
esrj-50337	265	9	and	and	CCONJ
esrj-50337	265	10	montecinos	montecinos	PROPN
esrj-50337	265	11	,	,	PUNCT
esrj-50337	265	12	s.	s.	PROPN
esrj-50337	265	13	(	(	PUNCT
esrj-50337	265	14	2014	2014	NUM
esrj-50337	265	15	)	)	PUNCT
esrj-50337	265	16	.	.	PUNCT
esrj-50337	266	1	hybrid	hybrid	ADJ
esrj-50337	266	2	neural	neural	ADJ
esrj-50337	266	3	network	network	NOUN
esrj-50337	266	4	–	–	PUNCT
esrj-50337	266	5	particle	particle	NOUN
esrj-50337	266	6	swarm	swarm	NOUN
esrj-50337	266	7	algorithm	algorithm	NOUN
esrj-50337	266	8	to	to	PART
esrj-50337	266	9	describe	describe	VERB
esrj-50337	266	10	chaotic	chaotic	ADJ
esrj-50337	266	11	time	time	NOUN
esrj-50337	266	12	series	series	NOUN
esrj-50337	266	13	.	.	PUNCT
esrj-50337	267	1	neural	neural	ADJ
esrj-50337	267	2	network	network	NOUN
esrj-50337	267	3	world	world	NOUN
esrj-50337	267	4	,	,	PUNCT
esrj-50337	267	5	24	24	NUM
esrj-50337	267	6	,	,	PUNCT
esrj-50337	267	7	601–617	601–617	NUM
esrj-50337	267	8	.	.	PUNCT
esrj-50337	268	1	liu	liu	PROPN
esrj-50337	268	2	,	,	PUNCT
esrj-50337	268	3	h.	h.	PROPN
esrj-50337	268	4	,	,	PUNCT
esrj-50337	268	5	tian	tian	PROPN
esrj-50337	268	6	,	,	PUNCT
esrj-50337	268	7	h.	h.	PROPN
esrj-50337	268	8	,	,	PUNCT
esrj-50337	268	9	pan	pan	PROPN
esrj-50337	268	10	,	,	PUNCT
esrj-50337	268	11	d.	d.	PROPN
esrj-50337	268	12	and	and	CCONJ
esrj-50337	268	13	li	li	PROPN
esrj-50337	268	14	,	,	PUNCT
esrj-50337	268	15	y.	y.	PROPN
esrj-50337	268	16	(	(	PUNCT
esrj-50337	268	17	2013	2013	NUM
esrj-50337	268	18	)	)	PUNCT
esrj-50337	268	19	.	.	PUNCT
esrj-50337	269	1	forecasting	forecasting	NOUN
esrj-50337	269	2	models	model	NOUN
esrj-50337	269	3	for	for	ADP
esrj-50337	269	4	wind	wind	NOUN
esrj-50337	269	5	speed	speed	NOUN
esrj-50337	269	6	using	use	VERB
esrj-50337	269	7	wavelet	wavelet	NOUN
esrj-50337	269	8	,	,	PUNCT
esrj-50337	269	9	wavelet	wavelet	NOUN
esrj-50337	269	10	packet	packet	NOUN
esrj-50337	269	11	,	,	PUNCT
esrj-50337	269	12	time	time	NOUN
esrj-50337	269	13	series	series	NOUN
esrj-50337	269	14	and	and	CCONJ
esrj-50337	269	15	artificial	artificial	ADJ
esrj-50337	269	16	neural	neural	ADJ
esrj-50337	269	17	networks	network	NOUN
esrj-50337	269	18	.	.	PUNCT
esrj-50337	270	1	applied	apply	VERB
esrj-50337	270	2	energy	energy	NOUN
esrj-50337	270	3	,	,	PUNCT
esrj-50337	270	4	107	107	NUM
esrj-50337	270	5	,	,	PUNCT
esrj-50337	270	6	191–208	191–208	NUM
esrj-50337	270	7	.	.	PUNCT
esrj-50337	271	1	mackey	mackey	PROPN
esrj-50337	271	2	,	,	PUNCT
esrj-50337	271	3	m.c	m.c	PROPN
esrj-50337	271	4	.	.	PROPN
esrj-50337	271	5	and	and	CCONJ
esrj-50337	271	6	glass	glass	NOUN
esrj-50337	271	7	,	,	PUNCT
esrj-50337	271	8	l.	l.	PROPN
esrj-50337	271	9	(	(	PUNCT
esrj-50337	271	10	1977	1977	NUM
esrj-50337	271	11	)	)	PUNCT
esrj-50337	271	12	.	.	PUNCT
esrj-50337	272	1	oscillation	oscillation	NOUN
esrj-50337	272	2	and	and	CCONJ
esrj-50337	272	3	chaos	chaos	NOUN
esrj-50337	272	4	in	in	ADP
esrj-50337	272	5	physiological	physiological	ADJ
esrj-50337	272	6	control	control	NOUN
esrj-50337	272	7	systems	system	NOUN
esrj-50337	272	8	.	.	PUNCT
esrj-50337	273	1	science	science	NOUN
esrj-50337	273	2	,	,	PUNCT
esrj-50337	273	3	197	197	NUM
esrj-50337	273	4	,	,	PUNCT
esrj-50337	273	5	287–289	287–289	NUM
esrj-50337	273	6	.	.	PUNCT
esrj-50337	274	1	martinetz	martinetz	ADJ
esrj-50337	274	2	,	,	PUNCT
esrj-50337	274	3	t.m	t.m	PROPN
esrj-50337	274	4	.	.	PROPN
esrj-50337	274	5	,	,	PUNCT
esrj-50337	274	6	berkovich	berkovich	PROPN
esrj-50337	274	7	,	,	PUNCT
esrj-50337	274	8	s.g	s.g	PROPN
esrj-50337	274	9	.	.	PROPN
esrj-50337	274	10	and	and	CCONJ
esrj-50337	274	11	schulten	schulten	VERB
esrj-50337	274	12	,	,	PUNCT
esrj-50337	274	13	k.j	k.j	PROPN
esrj-50337	274	14	.	.	PROPN
esrj-50337	275	1	(	(	PUNCT
esrj-50337	275	2	1993	1993	NUM
esrj-50337	275	3	)	)	PUNCT
esrj-50337	275	4	.	.	PUNCT
esrj-50337	276	1	neural	neural	ADJ
esrj-50337	276	2	-	-	PUNCT
esrj-50337	276	3	gas	gas	NOUN
esrj-50337	276	4	network	network	NOUN
esrj-50337	276	5	for	for	ADP
esrj-50337	276	6	vector	vector	NOUN
esrj-50337	276	7	quantization	quantization	NOUN
esrj-50337	276	8	and	and	CCONJ
esrj-50337	276	9	its	its	PRON
esrj-50337	276	10	application	application	NOUN
esrj-50337	276	11	to	to	ADP
esrj-50337	276	12	time	time	NOUN
esrj-50337	276	13	-	-	PUNCT
esrj-50337	276	14	series	series	NOUN
esrj-50337	276	15	prediction	prediction	NOUN
esrj-50337	276	16	.	.	PUNCT
esrj-50337	277	1	ieee	ieee	NOUN
esrj-50337	277	2	transactions	transaction	NOUN
esrj-50337	277	3	on	on	ADP
esrj-50337	277	4	neural	neural	ADJ
esrj-50337	277	5	networks	network	NOUN
esrj-50337	277	6	,	,	PUNCT
esrj-50337	277	7	4	4	NUM
esrj-50337	277	8	,	,	PUNCT
esrj-50337	277	9	558–569	558–569	NUM
esrj-50337	277	10	.	.	PUNCT
esrj-50337	278	1	meinen	meinen	PROPN
esrj-50337	278	2	,	,	PUNCT
esrj-50337	278	3	c.s	c.s	PROPN
esrj-50337	278	4	.	.	PROPN
esrj-50337	278	5	and	and	CCONJ
esrj-50337	278	6	mcphaden	mcphaden	ADJ
esrj-50337	278	7	,	,	PUNCT
esrj-50337	278	8	m.j	m.j	PROPN
esrj-50337	278	9	.	.	PROPN
esrj-50337	278	10	(	(	PUNCT
esrj-50337	278	11	2000	2000	NUM
esrj-50337	278	12	)	)	PUNCT
esrj-50337	278	13	.	.	PUNCT
esrj-50337	279	1	observations	observation	NOUN
esrj-50337	279	2	of	of	ADP
esrj-50337	279	3	warm	warm	ADJ
esrj-50337	279	4	water	water	NOUN
esrj-50337	279	5	volume	volume	NOUN
esrj-50337	279	6	changes	change	NOUN
esrj-50337	279	7	in	in	ADP
esrj-50337	279	8	the	the	DET
esrj-50337	279	9	equatorial	equatorial	ADJ
esrj-50337	279	10	pacific	pacific	NOUN
esrj-50337	279	11	and	and	CCONJ
esrj-50337	279	12	their	their	PRON
esrj-50337	279	13	relationship	relationship	NOUN
esrj-50337	279	14	to	to	ADP
esrj-50337	279	15	el	el	PROPN
esrj-50337	279	16	niño	niño	PROPN
esrj-50337	279	17	and	and	CCONJ
esrj-50337	279	18	la	la	PROPN
esrj-50337	279	19	niña	niña	PROPN
esrj-50337	279	20	.	.	PUNCT
esrj-50337	280	1	journal	journal	PROPN
esrj-50337	280	2	of	of	ADP
esrj-50337	280	3	climate	climate	NOUN
esrj-50337	280	4	,	,	PUNCT
esrj-50337	280	5	13	13	NUM
esrj-50337	280	6	,	,	PUNCT
esrj-50337	280	7	3551–3559	3551–3559	NUM
esrj-50337	280	8	.	.	PUNCT
esrj-50337	281	1	mirzaee	mirzaee	PROPN
esrj-50337	281	2	,	,	PUNCT
esrj-50337	281	3	h.	h.	PROPN
esrj-50337	281	4	(	(	PUNCT
esrj-50337	281	5	2009	2009	NUM
esrj-50337	281	6	)	)	PUNCT
esrj-50337	281	7	.	.	PUNCT
esrj-50337	282	1	linear	linear	ADJ
esrj-50337	282	2	combination	combination	NOUN
esrj-50337	282	3	rule	rule	NOUN
esrj-50337	282	4	in	in	ADP
esrj-50337	282	5	genetic	genetic	ADJ
esrj-50337	282	6	algorithm	algorithm	NOUN
esrj-50337	282	7	for	for	ADP
esrj-50337	282	8	optimization	optimization	NOUN
esrj-50337	282	9	of	of	ADP
esrj-50337	282	10	finite	finite	ADJ
esrj-50337	282	11	impulse	impulse	ADJ
esrj-50337	282	12	response	response	NOUN
esrj-50337	282	13	neural	neural	ADJ
esrj-50337	282	14	network	network	NOUN
esrj-50337	282	15	to	to	PART
esrj-50337	282	16	predict	predict	VERB
esrj-50337	282	17	natural	natural	ADJ
esrj-50337	282	18	chaotic	chaotic	ADJ
esrj-50337	282	19	time	time	NOUN
esrj-50337	282	20	series	series	NOUN
esrj-50337	282	21	.	.	PUNCT
esrj-50337	283	1	chaos	chaos	NOUN
esrj-50337	283	2	,	,	PUNCT
esrj-50337	283	3	solitons	soliton	NOUN
esrj-50337	283	4	&	&	CCONJ
esrj-50337	283	5	fractals	fractal	NOUN
esrj-50337	283	6	,	,	PUNCT
esrj-50337	283	7	41	41	NUM
esrj-50337	283	8	,	,	PUNCT
esrj-50337	283	9	2681–2689	2681–2689	NUM
esrj-50337	283	10	.	.	PUNCT
esrj-50337	283	11	monfared	monfare	VERB
esrj-50337	283	12	,	,	PUNCT
esrj-50337	283	13	m.	m.	NOUN
esrj-50337	283	14	,	,	PUNCT
esrj-50337	283	15	rastegar	rastegar	NOUN
esrj-50337	283	16	,	,	PUNCT
esrj-50337	283	17	h.	h.	PROPN
esrj-50337	283	18	and	and	CCONJ
esrj-50337	283	19	madadi	madadi	NOUN
esrj-50337	283	20	-	-	PUNCT
esrj-50337	283	21	kojabadi	kojabadi	NOUN
esrj-50337	283	22	,	,	PUNCT
esrj-50337	283	23	h.	h.	PROPN
esrj-50337	283	24	(	(	PUNCT
esrj-50337	283	25	2009	2009	NUM
esrj-50337	283	26	)	)	PUNCT
esrj-50337	283	27	.	.	PUNCT
esrj-50337	284	1	a	a	DET
esrj-50337	284	2	new	new	ADJ
esrj-50337	284	3	strategy	strategy	NOUN
esrj-50337	284	4	for	for	ADP
esrj-50337	284	5	wind	wind	NOUN
esrj-50337	284	6	speed	speed	NOUN
esrj-50337	284	7	forecasting	forecasting	NOUN
esrj-50337	284	8	using	use	VERB
esrj-50337	284	9	artificial	artificial	ADJ
esrj-50337	284	10	intelligent	intelligent	ADJ
esrj-50337	284	11	methods	method	NOUN
esrj-50337	284	12	.	.	PUNCT
esrj-50337	285	1	renewable	renewable	ADJ
esrj-50337	285	2	energy	energy	NOUN
esrj-50337	285	3	,	,	PUNCT
esrj-50337	285	4	34	34	NUM
esrj-50337	285	5	,	,	PUNCT
esrj-50337	285	6	845–848	845–848	NUM
esrj-50337	285	7	.	.	PUNCT
esrj-50337	286	1	pérez	pérez	NOUN
esrj-50337	286	2	ponce	ponce	PROPN
esrj-50337	286	3	,	,	PUNCT
esrj-50337	286	4	a.a	a.a	PROPN
esrj-50337	286	5	.	.	PROPN
esrj-50337	286	6	,	,	PUNCT
esrj-50337	286	7	lazzús	lazzús	PROPN
esrj-50337	286	8	,	,	PUNCT
esrj-50337	286	9	j.a	j.a	PROPN
esrj-50337	286	10	.	.	PROPN
esrj-50337	286	11	and	and	CCONJ
esrj-50337	286	12	palma	palma	NOUN
esrj-50337	286	13	-	-	PUNCT
esrj-50337	286	14	chilla	chilla	NOUN
esrj-50337	286	15	,	,	PUNCT
esrj-50337	286	16	l.	l.	PROPN
esrj-50337	286	17	hybrid	hybrid	ADJ
esrj-50337	286	18	neural	neural	ADJ
esrj-50337	286	19	network	network	NOUN
esrj-50337	286	20	–	–	PUNCT
esrj-50337	286	21	particle	particle	NOUN
esrj-50337	286	22	swarm	swarm	NOUN
esrj-50337	286	23	method	method	NOUN
esrj-50337	286	24	to	to	PART
esrj-50337	286	25	predict	predict	VERB
esrj-50337	286	26	global	global	ADJ
esrj-50337	286	27	radiation	radiation	NOUN
esrj-50337	286	28	over	over	ADP
esrj-50337	286	29	the	the	DET
esrj-50337	286	30	norte	norte	PROPN
esrj-50337	286	31	chico	chico	PROPN
esrj-50337	286	32	(	(	PUNCT
esrj-50337	286	33	chile	chile	PROPN
esrj-50337	286	34	)	)	PUNCT
esrj-50337	286	35	.	.	PUNCT
esrj-50337	287	1	journal	journal	NOUN
esrj-50337	287	2	of	of	ADP
esrj-50337	287	3	renewable	renewable	ADJ
esrj-50337	287	4	and	and	CCONJ
esrj-50337	287	5	sustainable	sustainable	ADJ
esrj-50337	287	6	energy	energy	NOUN
esrj-50337	287	7	,	,	PUNCT
esrj-50337	287	8	4	4	NUM
esrj-50337	287	9	,	,	PUNCT
esrj-50337	287	10	023108	023108	NUM
esrj-50337	287	11	.	.	PUNCT
esrj-50337	288	1	velo	velo	PROPN
esrj-50337	288	2	,	,	PUNCT
esrj-50337	288	3	r.	r.	PROPN
esrj-50337	288	4	,	,	PUNCT
esrj-50337	288	5	lópez	lópez	NOUN
esrj-50337	288	6	,	,	PUNCT
esrj-50337	288	7	p.	p.	NOUN
esrj-50337	288	8	and	and	CCONJ
esrj-50337	288	9	maseda	maseda	PROPN
esrj-50337	288	10	,	,	PUNCT
esrj-50337	288	11	f.	f.	PROPN
esrj-50337	288	12	(	(	PUNCT
esrj-50337	288	13	2014	2014	NUM
esrj-50337	288	14	)	)	PUNCT
esrj-50337	288	15	.	.	PUNCT
esrj-50337	289	1	wind	wind	NOUN
esrj-50337	289	2	speed	speed	NOUN
esrj-50337	289	3	estimation	estimation	NOUN
esrj-50337	289	4	using	use	VERB
esrj-50337	289	5	multilayer	multilayer	PROPN
esrj-50337	289	6	perceptron	perceptron	PROPN
esrj-50337	289	7	.	.	PUNCT
esrj-50337	290	1	energy	energy	NOUN
esrj-50337	290	2	conversion	conversion	NOUN
esrj-50337	290	3	and	and	CCONJ
esrj-50337	290	4	management	management	NOUN
esrj-50337	290	5	,	,	PUNCT
esrj-50337	290	6	81	81	NUM
esrj-50337	290	7	,	,	PUNCT
esrj-50337	290	8	1–9	1–9	PROPN
esrj-50337	290	9	.	.	PROPN
esrj-50337	290	10	wang	wang	PROPN
esrj-50337	290	11	,	,	PUNCT
esrj-50337	290	12	j.	j.	PROPN
esrj-50337	290	13	,	,	PUNCT
esrj-50337	290	14	zhang	zhang	PROPN
esrj-50337	290	15	,	,	PUNCT
esrj-50337	290	16	w.	w.	PROPN
esrj-50337	290	17	,	,	PUNCT
esrj-50337	290	18	wang	wang	PROPN
esrj-50337	290	19	,	,	PUNCT
esrj-50337	290	20	j.	j.	PROPN
esrj-50337	290	21	,	,	PUNCT
esrj-50337	290	22	han	han	PROPN
esrj-50337	290	23	,	,	PUNCT
esrj-50337	290	24	t.	t.	PROPN
esrj-50337	290	25	and	and	CCONJ
esrj-50337	290	26	kong	kong	PROPN
esrj-50337	290	27	,	,	PUNCT
esrj-50337	290	28	l.	l.	PROPN
esrj-50337	290	29	a	a	DET
esrj-50337	290	30	novel	novel	ADJ
esrj-50337	290	31	hybrid	hybrid	NOUN
esrj-50337	290	32	approach	approach	NOUN
esrj-50337	290	33	for	for	ADP
esrj-50337	290	34	wind	wind	NOUN
esrj-50337	290	35	speed	speed	NOUN
esrj-50337	290	36	prediction	prediction	NOUN
esrj-50337	290	37	.	.	PUNCT
esrj-50337	291	1	information	information	NOUN
esrj-50337	291	2	sciences	sciences	PROPN
esrj-50337	291	3	,	,	PUNCT
esrj-50337	291	4	273	273	NUM
esrj-50337	291	5	,	,	PUNCT
esrj-50337	291	6	304–318	304–318	NUM
esrj-50337	291	7	.	.	PUNCT
esrj-50337	292	1	whitehead	whitehead	PROPN
esrj-50337	292	2	,	,	PUNCT
esrj-50337	292	3	b.a	b.a	PROPN
esrj-50337	292	4	.	.	PROPN
esrj-50337	292	5	and	and	CCONJ
esrj-50337	292	6	choate	choate	PROPN
esrj-50337	292	7	,	,	PUNCT
esrj-50337	292	8	t.d	t.d	PROPN
esrj-50337	292	9	.	.	PUNCT
esrj-50337	292	10	(	(	PUNCT
esrj-50337	292	11	1996	1996	NUM
esrj-50337	292	12	)	)	PUNCT
esrj-50337	292	13	.	.	PUNCT
esrj-50337	293	1	cooperative	cooperative	ADJ
esrj-50337	293	2	-	-	PUNCT
esrj-50337	293	3	competitive	competitive	ADJ
esrj-50337	293	4	genetic	genetic	ADJ
esrj-50337	293	5	evolution	evolution	NOUN
esrj-50337	293	6	of	of	ADP
esrj-50337	293	7	radial	radial	ADJ
esrj-50337	293	8	basis	basis	NOUN
esrj-50337	293	9	function	function	NOUN
esrj-50337	293	10	centers	center	NOUN
esrj-50337	293	11	and	and	CCONJ
esrj-50337	293	12	widths	width	NOUN
esrj-50337	293	13	for	for	ADP
esrj-50337	293	14	time	time	NOUN
esrj-50337	293	15	series	series	PROPN
esrj-50337	293	16	prediction	prediction	PROPN
esrj-50337	293	17	.	.	PUNCT
esrj-50337	294	1	ieee	ieee	NOUN
esrj-50337	294	2	transactions	transaction	NOUN
esrj-50337	294	3	on	on	ADP
esrj-50337	294	4	neural	neural	ADJ
esrj-50337	294	5	networks	network	NOUN
esrj-50337	294	6	,	,	PUNCT
esrj-50337	294	7	7	7	NUM
esrj-50337	294	8	,	,	PUNCT
esrj-50337	294	9	869–880	869–880	NUM
esrj-50337	294	10	.	.	PUNCT
esrj-50337	295	1	zhang	zhang	PROPN
esrj-50337	295	2	,	,	PUNCT
esrj-50337	295	3	w.	w.	PROPN
esrj-50337	295	4	,	,	PUNCT
esrj-50337	295	5	wu	wu	PROPN
esrj-50337	295	6	,	,	PUNCT
esrj-50337	295	7	j.	j.	PROPN
esrj-50337	295	8	,	,	PUNCT
esrj-50337	295	9	wang	wang	PROPN
esrj-50337	295	10	,	,	PUNCT
esrj-50337	295	11	j.	j.	PROPN
esrj-50337	295	12	,	,	PUNCT
esrj-50337	295	13	zhao	zhao	PROPN
esrj-50337	295	14	,	,	PUNCT
esrj-50337	295	15	w.	w.	PROPN
esrj-50337	295	16	and	and	CCONJ
esrj-50337	295	17	shen	shen	PROPN
esrj-50337	295	18	,	,	PUNCT
esrj-50337	295	19	j.	j.	PROPN
esrj-50337	295	20	(	(	PUNCT
esrj-50337	295	21	2012	2012	NUM
esrj-50337	295	22	)	)	PUNCT
esrj-50337	295	23	.	.	PUNCT
esrj-50337	296	1	performance	performance	NOUN
esrj-50337	296	2	analysis	analysis	NOUN
esrj-50337	296	3	of	of	ADP
esrj-50337	296	4	four	four	NUM
esrj-50337	296	5	modified	modified	ADJ
esrj-50337	296	6	approaches	approach	NOUN
esrj-50337	296	7	for	for	ADP
esrj-50337	296	8	wind	wind	NOUN
esrj-50337	296	9	speed	speed	NOUN
esrj-50337	296	10	forecasting	forecasting	NOUN
esrj-50337	296	11	.	.	PUNCT
esrj-50337	297	1	applied	apply	VERB
esrj-50337	297	2	energy	energy	NOUN
esrj-50337	297	3	,	,	PUNCT
esrj-50337	297	4	99	99	NUM
esrj-50337	297	5	,	,	PUNCT
esrj-50337	297	6	324–333	324–333	NUM
esrj-50337	297	7	.	.	PUNCT