id	sid	tid	token	lemma	pos
fcis-27234	1	1	frontiers	frontier	NOUN
fcis-27234	1	2	in	in	ADP
fcis-27234	1	3	computing	computing	NOUN
fcis-27234	1	4	and	and	CCONJ
fcis-27234	1	5	intelligent	intelligent	ADJ
fcis-27234	1	6	systems	system	NOUN
fcis-27234	1	7	issn	issn	VERB
fcis-27234	1	8	:	:	PUNCT
fcis-27234	1	9	2832	2832	NUM
fcis-27234	1	10	-	-	SYM
fcis-27234	1	11	6024	6024	NUM
fcis-27234	1	12	|	|	NOUN
fcis-27234	1	13	vol	vol	NOUN
fcis-27234	1	14	.	.	PROPN
fcis-27234	2	1	10	10	NUM
fcis-27234	2	2	,	,	PUNCT
fcis-27234	2	3	no	no	INTJ
fcis-27234	2	4	.	.	NOUN
fcis-27234	2	5	2	2	NUM
fcis-27234	2	6	,	,	PUNCT
fcis-27234	2	7	2024	2024	NUM
fcis-27234	2	8	50	50	NUM
fcis-27234	2	9	prediction	prediction	NOUN
fcis-27234	2	10	of	of	ADP
fcis-27234	2	11	sunspot	sunspot	NOUN
fcis-27234	2	12	activity	activity	NOUN
fcis-27234	2	13	based	base	VERB
fcis-27234	2	14	on	on	ADP
fcis-27234	2	15	differential	differential	ADJ
fcis-27234	2	16	evolution	evolution	NOUN
fcis-27234	2	17	algorithm	algorithm	NOUN
fcis-27234	2	18	and	and	CCONJ
fcis-27234	2	19	bp	bp	PROPN
fcis-27234	2	20	neural	neural	PROPN
fcis-27234	2	21	network	network	PROPN
fcis-27234	2	22	model	model	PROPN
fcis-27234	2	23	ziyang	ziyang	PROPN
fcis-27234	2	24	yu	yu	PROPN
fcis-27234	2	25	1	1	NUM
fcis-27234	2	26	,	,	PUNCT
fcis-27234	2	27	xinpeng	xinpeng	PROPN
fcis-27234	2	28	yu	yu	PROPN
fcis-27234	2	29	2	2	NUM
fcis-27234	2	30	,	,	PUNCT
fcis-27234	2	31	yinuo	yinuo	PROPN
fcis-27234	2	32	liu	liu	PROPN
fcis-27234	2	33	3	3	PROPN
fcis-27234	2	34	,	,	PUNCT
fcis-27234	2	35	yue	yue	PROPN
fcis-27234	2	36	li	li	PROPN
fcis-27234	2	37	4	4	PROPN
fcis-27234	2	38	,	,	PUNCT
fcis-27234	2	39	yuyan	yuyan	PROPN
fcis-27234	2	40	zeng	zeng	PROPN
fcis-27234	2	41	1	1	NUM
fcis-27234	2	42	,	,	PUNCT
fcis-27234	2	43	yanqing	yanqe	VERB
fcis-27234	2	44	liu	liu	PROPN
fcis-27234	2	45	1	1	NUM
fcis-27234	2	46	,	,	PUNCT
fcis-27234	2	47	*	*	PUNCT
fcis-27234	2	48	1	1	NUM
fcis-27234	2	49	school	school	NOUN
fcis-27234	2	50	of	of	ADP
fcis-27234	2	51	information	information	NOUN
fcis-27234	2	52	engineering	engineering	NOUN
fcis-27234	2	53	,	,	PUNCT
fcis-27234	2	54	guilin	guilin	PROPN
fcis-27234	2	55	institute	institute	PROPN
fcis-27234	2	56	of	of	ADP
fcis-27234	2	57	information	information	PROPN
fcis-27234	2	58	technology	technology	PROPN
fcis-27234	2	59	,	,	PUNCT
fcis-27234	2	60	guilin	guilin	PROPN
fcis-27234	2	61	,	,	PUNCT
fcis-27234	2	62	guangxi	guangxi	PROPN
fcis-27234	2	63	,	,	PUNCT
fcis-27234	2	64	china	china	PROPN
fcis-27234	2	65	2	2	NUM
fcis-27234	2	66	school	school	NOUN
fcis-27234	2	67	of	of	ADP
fcis-27234	2	68	electronic	electronic	ADJ
fcis-27234	2	69	engineering	engineering	NOUN
fcis-27234	2	70	,	,	PUNCT
fcis-27234	2	71	guilin	guilin	PROPN
fcis-27234	2	72	institute	institute	PROPN
fcis-27234	2	73	of	of	ADP
fcis-27234	2	74	information	information	PROPN
fcis-27234	2	75	technology	technology	PROPN
fcis-27234	2	76	,	,	PUNCT
fcis-27234	2	77	guilin	guilin	PROPN
fcis-27234	2	78	,	,	PUNCT
fcis-27234	2	79	guangxi	guangxi	PROPN
fcis-27234	2	80	,	,	PUNCT
fcis-27234	2	81	china	china	PROPN
fcis-27234	2	82	3	3	NUM
fcis-27234	2	83	school	school	NOUN
fcis-27234	2	84	of	of	ADP
fcis-27234	2	85	foreign	foreign	ADJ
fcis-27234	2	86	trade	trade	NOUN
fcis-27234	2	87	and	and	CCONJ
fcis-27234	2	88	foreign	foreign	ADJ
fcis-27234	2	89	languages	language	NOUN
fcis-27234	2	90	,	,	PUNCT
fcis-27234	2	91	guilin	guilin	PROPN
fcis-27234	2	92	institute	institute	PROPN
fcis-27234	2	93	of	of	ADP
fcis-27234	2	94	information	information	PROPN
fcis-27234	2	95	technology	technology	PROPN
fcis-27234	2	96	,	,	PUNCT
fcis-27234	2	97	guilin	guilin	PROPN
fcis-27234	2	98	,	,	PUNCT
fcis-27234	2	99	guangxi	guangxi	PROPN
fcis-27234	2	100	,	,	PUNCT
fcis-27234	2	101	china	china	PROPN
fcis-27234	2	102	4	4	NUM
fcis-27234	2	103	school	school	NOUN
fcis-27234	2	104	of	of	ADP
fcis-27234	2	105	mechanical	mechanical	ADJ
fcis-27234	2	106	and	and	CCONJ
fcis-27234	2	107	electrical	electrical	ADJ
fcis-27234	2	108	engineering	engineering	NOUN
fcis-27234	2	109	,	,	PUNCT
fcis-27234	2	110	guilin	guilin	PROPN
fcis-27234	2	111	institute	institute	PROPN
fcis-27234	2	112	of	of	ADP
fcis-27234	2	113	information	information	PROPN
fcis-27234	2	114	technology	technology	PROPN
fcis-27234	2	115	,	,	PUNCT
fcis-27234	2	116	guilin	guilin	PROPN
fcis-27234	2	117	,	,	PUNCT
fcis-27234	2	118	guangxi	guangxi	PROPN
fcis-27234	2	119	,	,	PUNCT
fcis-27234	2	120	china	china	PROPN
fcis-27234	2	121	*	*	PUNCT
fcis-27234	2	122	corresponding	correspond	VERB
fcis-27234	2	123	author	author	NOUN
fcis-27234	2	124	:	:	PUNCT
fcis-27234	2	125	yanqing	yanqe	VERB
fcis-27234	2	126	liu	liu	PROPN
fcis-27234	2	127	(	(	PUNCT
fcis-27234	2	128	email	email	NOUN
fcis-27234	2	129	:	:	PUNCT
fcis-27234	2	130	837104905@qq.com	837104905@qq.com	NUM
fcis-27234	2	131	)	)	PUNCT
fcis-27234	2	132	abstract	abstract	NOUN
fcis-27234	2	133	:	:	PUNCT
fcis-27234	2	134	in	in	ADP
fcis-27234	2	135	this	this	DET
fcis-27234	2	136	research	research	NOUN
fcis-27234	2	137	report	report	NOUN
fcis-27234	2	138	,	,	PUNCT
fcis-27234	2	139	the	the	DET
fcis-27234	2	140	prediction	prediction	NOUN
fcis-27234	2	141	method	method	NOUN
fcis-27234	2	142	of	of	ADP
fcis-27234	2	143	sunspot	sunspot	NOUN
fcis-27234	2	144	activity	activity	NOUN
fcis-27234	2	145	is	be	AUX
fcis-27234	2	146	deeply	deeply	ADV
fcis-27234	2	147	discussed	discuss	VERB
fcis-27234	2	148	,	,	PUNCT
fcis-27234	2	149	and	and	CCONJ
fcis-27234	2	150	a	a	DET
fcis-27234	2	151	variety	variety	NOUN
fcis-27234	2	152	of	of	ADP
fcis-27234	2	153	statistical	statistical	ADJ
fcis-27234	2	154	and	and	CCONJ
fcis-27234	2	155	data	datum	NOUN
fcis-27234	2	156	science	science	NOUN
fcis-27234	2	157	models	model	NOUN
fcis-27234	2	158	are	be	AUX
fcis-27234	2	159	used	use	VERB
fcis-27234	2	160	to	to	PART
fcis-27234	2	161	make	make	VERB
fcis-27234	2	162	comprehensive	comprehensive	ADJ
fcis-27234	2	163	prediction	prediction	NOUN
fcis-27234	2	164	.	.	PUNCT
fcis-27234	3	1	research	research	NOUN
fcis-27234	3	2	focuses	focus	VERB
fcis-27234	3	3	include	include	VERB
fcis-27234	3	4	the	the	DET
fcis-27234	3	5	start	start	ADJ
fcis-27234	3	6	time	time	NOUN
fcis-27234	3	7	and	and	CCONJ
fcis-27234	3	8	duration	duration	NOUN
fcis-27234	3	9	of	of	ADP
fcis-27234	3	10	the	the	DET
fcis-27234	3	11	solar	solar	ADJ
fcis-27234	3	12	maximum	maximum	NOUN
fcis-27234	3	13	in	in	ADP
fcis-27234	3	14	the	the	DET
fcis-27234	3	15	solar	solar	ADJ
fcis-27234	3	16	cycle	cycle	NOUN
fcis-27234	3	17	,	,	PUNCT
fcis-27234	3	18	as	as	ADV
fcis-27234	3	19	well	well	ADV
fcis-27234	3	20	as	as	ADP
fcis-27234	3	21	the	the	DET
fcis-27234	3	22	prediction	prediction	NOUN
fcis-27234	3	23	of	of	ADP
fcis-27234	3	24	the	the	DET
fcis-27234	3	25	number	number	NOUN
fcis-27234	3	26	and	and	CCONJ
fcis-27234	3	27	area	area	NOUN
fcis-27234	3	28	of	of	ADP
fcis-27234	3	29	sunspots	sunspot	NOUN
fcis-27234	3	30	.	.	PUNCT
fcis-27234	4	1	by	by	ADP
fcis-27234	4	2	capturing	capture	VERB
fcis-27234	4	3	the	the	DET
fcis-27234	4	4	periodicity	periodicity	NOUN
fcis-27234	4	5	of	of	ADP
fcis-27234	4	6	solar	solar	ADJ
fcis-27234	4	7	activity	activity	NOUN
fcis-27234	4	8	,	,	PUNCT
fcis-27234	4	9	pearson	pearson	NOUN
fcis-27234	4	10	correlation	correlation	NOUN
fcis-27234	4	11	coefficient	coefficient	NOUN
fcis-27234	4	12	is	be	AUX
fcis-27234	4	13	used	use	VERB
fcis-27234	4	14	to	to	PART
fcis-27234	4	15	analyze	analyze	VERB
fcis-27234	4	16	the	the	DET
fcis-27234	4	17	relationship	relationship	NOUN
fcis-27234	4	18	between	between	ADP
fcis-27234	4	19	the	the	DET
fcis-27234	4	20	maximum	maximum	ADJ
fcis-27234	4	21	solar	solar	ADJ
fcis-27234	4	22	activity	activity	NOUN
fcis-27234	4	23	and	and	CCONJ
fcis-27234	4	24	the	the	DET
fcis-27234	4	25	number	number	NOUN
fcis-27234	4	26	of	of	ADP
fcis-27234	4	27	sunspots	sunspot	NOUN
fcis-27234	4	28	,	,	PUNCT
fcis-27234	4	29	and	and	CCONJ
fcis-27234	4	30	the	the	DET
fcis-27234	4	31	adaptive	adaptive	ADJ
fcis-27234	4	32	multivariate	multivariate	NOUN
fcis-27234	4	33	nonlinear	nonlinear	ADJ
fcis-27234	4	34	regression	regression	NOUN
fcis-27234	4	35	-	-	PUNCT
fcis-27234	4	36	bp	bp	PROPN
fcis-27234	4	37	neural	neural	ADJ
fcis-27234	4	38	network	network	NOUN
fcis-27234	4	39	model	model	NOUN
fcis-27234	4	40	and	and	CCONJ
fcis-27234	4	41	particle	particle	NOUN
fcis-27234	4	42	swarm	swarm	NOUN
fcis-27234	4	43	optimization	optimization	PROPN
fcis-27234	4	44	bp	bp	PROPN
fcis-27234	4	45	neural	neural	ADJ
fcis-27234	4	46	network	network	NOUN
fcis-27234	4	47	are	be	AUX
fcis-27234	4	48	combined	combine	VERB
fcis-27234	4	49	to	to	PART
fcis-27234	4	50	make	make	VERB
fcis-27234	4	51	high	high	ADJ
fcis-27234	4	52	-	-	PUNCT
fcis-27234	4	53	precision	precision	NOUN
fcis-27234	4	54	prediction	prediction	NOUN
fcis-27234	4	55	.	.	PUNCT
fcis-27234	5	1	the	the	DET
fcis-27234	5	2	research	research	NOUN
fcis-27234	5	3	results	result	NOUN
fcis-27234	5	4	provide	provide	VERB
fcis-27234	5	5	a	a	DET
fcis-27234	5	6	scientific	scientific	ADJ
fcis-27234	5	7	basis	basis	NOUN
fcis-27234	5	8	for	for	ADP
fcis-27234	5	9	the	the	DET
fcis-27234	5	10	prediction	prediction	NOUN
fcis-27234	5	11	of	of	ADP
fcis-27234	5	12	space	space	NOUN
fcis-27234	5	13	weather	weather	NOUN
fcis-27234	5	14	,	,	PUNCT
fcis-27234	5	15	ionospheric	ionospheric	ADJ
fcis-27234	5	16	state	state	NOUN
fcis-27234	5	17	and	and	CCONJ
fcis-27234	5	18	communication	communication	NOUN
fcis-27234	5	19	system	system	NOUN
fcis-27234	5	20	reliability	reliability	NOUN
fcis-27234	5	21	.	.	PUNCT
fcis-27234	6	1	keywords	keyword	NOUN
fcis-27234	6	2	:	:	PUNCT
fcis-27234	6	3	neural	neural	ADJ
fcis-27234	6	4	network	network	NOUN
fcis-27234	6	5	;	;	PUNCT
fcis-27234	6	6	pearson	pearson	PROPN
fcis-27234	6	7	correlation	correlation	NOUN
fcis-27234	6	8	coefficient	coefficient	NOUN
fcis-27234	6	9	;	;	PUNCT
fcis-27234	6	10	differential	differential	ADJ
fcis-27234	6	11	evolution	evolution	NOUN
fcis-27234	6	12	algorithm	algorithm	NOUN
fcis-27234	6	13	;	;	PUNCT
fcis-27234	6	14	particle	particle	NOUN
fcis-27234	6	15	swarm	swarm	NOUN
fcis-27234	6	16	optimization	optimization	NOUN
fcis-27234	6	17	.	.	PUNCT
fcis-27234	7	1	1	1	X
fcis-27234	7	2	.	.	X
fcis-27234	7	3	introduction	introduction	NOUN
fcis-27234	7	4	this	this	DET
fcis-27234	7	5	research	research	NOUN
fcis-27234	7	6	report	report	NOUN
fcis-27234	7	7	aims	aim	VERB
fcis-27234	7	8	to	to	PART
fcis-27234	7	9	analyze	analyze	VERB
fcis-27234	7	10	and	and	CCONJ
fcis-27234	7	11	predict	predict	VERB
fcis-27234	7	12	the	the	DET
fcis-27234	7	13	key	key	ADJ
fcis-27234	7	14	features	feature	NOUN
fcis-27234	7	15	of	of	ADP
fcis-27234	7	16	sunspot	sunspot	NOUN
fcis-27234	7	17	activity	activity	NOUN
fcis-27234	7	18	in	in	ADP
fcis-27234	7	19	depth	depth	NOUN
fcis-27234	7	20	.	.	PUNCT
fcis-27234	8	1	sunspots	sunspot	NOUN
fcis-27234	8	2	are	be	AUX
fcis-27234	8	3	temporary	temporary	ADJ
fcis-27234	8	4	spots	spot	NOUN
fcis-27234	8	5	on	on	ADP
fcis-27234	8	6	the	the	DET
fcis-27234	8	7	photosphere	photosphere	NOUN
fcis-27234	8	8	whose	whose	DET
fcis-27234	8	9	number	number	NOUN
fcis-27234	8	10	and	and	CCONJ
fcis-27234	8	11	area	area	NOUN
fcis-27234	8	12	changes	change	NOUN
fcis-27234	8	13	are	be	AUX
fcis-27234	8	14	closely	closely	ADV
fcis-27234	8	15	related	relate	VERB
fcis-27234	8	16	to	to	ADP
fcis-27234	8	17	the	the	DET
fcis-27234	8	18	periodicity	periodicity	NOUN
fcis-27234	8	19	of	of	ADP
fcis-27234	8	20	solar	solar	ADJ
fcis-27234	8	21	activity	activity	NOUN
fcis-27234	8	22	[	[	X
fcis-27234	8	23	1	1	NUM
fcis-27234	8	24	]	]	PUNCT
fcis-27234	8	25	.	.	PUNCT
fcis-27234	9	1	to	to	PART
fcis-27234	9	2	better	well	ADV
fcis-27234	9	3	understand	understand	VERB
fcis-27234	9	4	and	and	CCONJ
fcis-27234	9	5	predict	predict	VERB
fcis-27234	9	6	sunspot	sunspot	NOUN
fcis-27234	9	7	activity	activity	NOUN
fcis-27234	9	8	,	,	PUNCT
fcis-27234	9	9	this	this	DET
fcis-27234	9	10	study	study	NOUN
fcis-27234	9	11	used	use	VERB
fcis-27234	9	12	a	a	DET
fcis-27234	9	13	variety	variety	NOUN
fcis-27234	9	14	of	of	ADP
fcis-27234	9	15	statistical	statistical	ADJ
fcis-27234	9	16	and	and	CCONJ
fcis-27234	9	17	data	datum	NOUN
fcis-27234	9	18	science	science	NOUN
fcis-27234	9	19	models	model	NOUN
fcis-27234	9	20	.	.	PUNCT
fcis-27234	10	1	specifically	specifically	ADV
fcis-27234	10	2	,	,	PUNCT
fcis-27234	10	3	we	we	PRON
fcis-27234	10	4	used	use	VERB
fcis-27234	10	5	pearson	pearson	PROPN
fcis-27234	10	6	correlation	correlation	NOUN
fcis-27234	10	7	coefficient	coefficient	NOUN
fcis-27234	10	8	analysis	analysis	NOUN
fcis-27234	10	9	to	to	PART
fcis-27234	10	10	explore	explore	VERB
fcis-27234	10	11	the	the	DET
fcis-27234	10	12	relationship	relationship	NOUN
fcis-27234	10	13	between	between	ADP
fcis-27234	10	14	solar	solar	ADJ
fcis-27234	10	15	maximum	maximum	ADJ
fcis-27234	10	16	duration	duration	NOUN
fcis-27234	10	17	and	and	CCONJ
fcis-27234	10	18	sunspot	sunspot	NOUN
fcis-27234	10	19	number	number	NOUN
fcis-27234	10	20	[	[	X
fcis-27234	10	21	2	2	NUM
fcis-27234	10	22	]	]	PUNCT
fcis-27234	10	23	,	,	PUNCT
fcis-27234	10	24	and	and	CCONJ
fcis-27234	10	25	built	build	VERB
fcis-27234	10	26	a	a	DET
fcis-27234	10	27	prediction	prediction	NOUN
fcis-27234	10	28	system	system	NOUN
fcis-27234	10	29	[	[	X
fcis-27234	10	30	3	3	NUM
fcis-27234	10	31	]	]	PUNCT
fcis-27234	10	32	based	base	VERB
fcis-27234	10	33	on	on	ADP
fcis-27234	10	34	the	the	DET
fcis-27234	10	35	adaptive	adaptive	ADJ
fcis-27234	10	36	multivariate	multivariate	NOUN
fcis-27234	10	37	nonlinear	nonlinear	ADJ
fcis-27234	10	38	regression	regression	NOUN
fcis-27234	10	39	-	-	PUNCT
fcis-27234	10	40	bp	bp	PROPN
fcis-27234	10	41	neural	neural	ADJ
fcis-27234	10	42	network	network	NOUN
fcis-27234	10	43	model	model	NOUN
fcis-27234	10	44	to	to	PART
fcis-27234	10	45	predict	predict	VERB
fcis-27234	10	46	the	the	DET
fcis-27234	10	47	start	start	ADJ
fcis-27234	10	48	time	time	NOUN
fcis-27234	10	49	and	and	CCONJ
fcis-27234	10	50	duration	duration	NOUN
fcis-27234	10	51	of	of	ADP
fcis-27234	10	52	the	the	DET
fcis-27234	10	53	solar	solar	ADJ
fcis-27234	10	54	maximum	maximum	NOUN
fcis-27234	10	55	in	in	ADP
fcis-27234	10	56	the	the	DET
fcis-27234	10	57	next	next	ADJ
fcis-27234	10	58	solar	solar	ADJ
fcis-27234	10	59	cycle	cycle	NOUN
fcis-27234	10	60	.	.	PUNCT
fcis-27234	11	1	in	in	ADP
fcis-27234	11	2	addition	addition	NOUN
fcis-27234	11	3	,	,	PUNCT
fcis-27234	11	4	in	in	ADP
fcis-27234	11	5	order	order	NOUN
fcis-27234	11	6	to	to	PART
fcis-27234	11	7	more	more	ADV
fcis-27234	11	8	accurately	accurately	ADV
fcis-27234	11	9	predict	predict	VERB
fcis-27234	11	10	the	the	DET
fcis-27234	11	11	changing	change	VERB
fcis-27234	11	12	trend	trend	NOUN
fcis-27234	11	13	of	of	ADP
fcis-27234	11	14	sunspot	sunspot	NOUN
fcis-27234	11	15	number	number	NOUN
fcis-27234	11	16	and	and	CCONJ
fcis-27234	11	17	area	area	NOUN
fcis-27234	11	18	,	,	PUNCT
fcis-27234	11	19	we	we	PRON
fcis-27234	11	20	introduced	introduce	VERB
fcis-27234	11	21	the	the	DET
fcis-27234	11	22	time	time	NOUN
fcis-27234	11	23	series	series	PROPN
fcis-27234	11	24	prediction	prediction	NOUN
fcis-27234	11	25	model	model	NOUN
fcis-27234	11	26	of	of	ADP
fcis-27234	11	27	bp	bp	PROPN
fcis-27234	11	28	neural	neural	PROPN
fcis-27234	11	29	network	network	NOUN
fcis-27234	11	30	based	base	VERB
fcis-27234	11	31	on	on	ADP
fcis-27234	11	32	particle	particle	NOUN
fcis-27234	11	33	swarm	swarm	NOUN
fcis-27234	11	34	optimization	optimization	NOUN
fcis-27234	11	35	[	[	X
fcis-27234	11	36	4	4	NUM
fcis-27234	11	37	]	]	PUNCT
fcis-27234	11	38	.	.	PUNCT
fcis-27234	12	1	this	this	DET
fcis-27234	12	2	model	model	NOUN
fcis-27234	12	3	combines	combine	VERB
fcis-27234	12	4	the	the	DET
fcis-27234	12	5	prediction	prediction	NOUN
fcis-27234	12	6	ability	ability	NOUN
fcis-27234	12	7	of	of	ADP
fcis-27234	12	8	bp	bp	PROPN
fcis-27234	12	9	neural	neural	ADJ
fcis-27234	12	10	network	network	NOUN
fcis-27234	12	11	and	and	CCONJ
fcis-27234	12	12	the	the	DET
fcis-27234	12	13	optimization	optimization	NOUN
fcis-27234	12	14	effect	effect	NOUN
fcis-27234	12	15	of	of	ADP
fcis-27234	12	16	pso	pso	NOUN
fcis-27234	12	17	algorithm	algorithm	NOUN
fcis-27234	12	18	[	[	X
fcis-27234	12	19	5	5	NUM
fcis-27234	12	20	]	]	PUNCT
fcis-27234	12	21	,	,	PUNCT
fcis-27234	12	22	and	and	CCONJ
fcis-27234	12	23	can	can	AUX
fcis-27234	12	24	more	more	ADV
fcis-27234	12	25	accurately	accurately	ADV
fcis-27234	12	26	reflect	reflect	VERB
fcis-27234	12	27	the	the	DET
fcis-27234	12	28	dynamic	dynamic	ADJ
fcis-27234	12	29	changes	change	NOUN
fcis-27234	12	30	of	of	ADP
fcis-27234	12	31	sunspot	sunspot	NOUN
fcis-27234	12	32	activity	activity	NOUN
fcis-27234	12	33	.	.	PUNCT
fcis-27234	13	1	through	through	ADP
fcis-27234	13	2	the	the	DET
fcis-27234	13	3	application	application	NOUN
fcis-27234	13	4	of	of	ADP
fcis-27234	13	5	these	these	DET
fcis-27234	13	6	comprehensive	comprehensive	ADJ
fcis-27234	13	7	methods	method	NOUN
fcis-27234	13	8	,	,	PUNCT
fcis-27234	13	9	it	it	PRON
fcis-27234	13	10	is	be	AUX
fcis-27234	13	11	expected	expect	VERB
fcis-27234	13	12	to	to	PART
fcis-27234	13	13	provide	provide	VERB
fcis-27234	13	14	a	a	DET
fcis-27234	13	15	more	more	ADV
fcis-27234	13	16	accurate	accurate	ADJ
fcis-27234	13	17	and	and	CCONJ
fcis-27234	13	18	reliable	reliable	ADJ
fcis-27234	13	19	basis	basis	NOUN
fcis-27234	13	20	for	for	ADP
fcis-27234	13	21	the	the	DET
fcis-27234	13	22	prediction	prediction	NOUN
fcis-27234	13	23	of	of	ADP
fcis-27234	13	24	sunspot	sunspot	NOUN
fcis-27234	13	25	activity	activity	NOUN
fcis-27234	13	26	,	,	PUNCT
fcis-27234	13	27	and	and	CCONJ
fcis-27234	13	28	further	far	ADV
fcis-27234	13	29	promote	promote	VERB
fcis-27234	13	30	the	the	DET
fcis-27234	13	31	development	development	NOUN
fcis-27234	13	32	of	of	ADP
fcis-27234	13	33	solar	solar	ADJ
fcis-27234	13	34	physics	physics	NOUN
fcis-27234	13	35	,	,	PUNCT
fcis-27234	13	36	space	space	NOUN
fcis-27234	13	37	weather	weather	NOUN
fcis-27234	13	38	prediction	prediction	NOUN
fcis-27234	13	39	,	,	PUNCT
fcis-27234	13	40	communication	communication	NOUN
fcis-27234	13	41	technology	technology	NOUN
fcis-27234	13	42	and	and	CCONJ
fcis-27234	13	43	other	other	ADJ
fcis-27234	13	44	related	related	ADJ
fcis-27234	13	45	fields	field	NOUN
fcis-27234	13	46	.	.	PUNCT
fcis-27234	14	1	2	2	X
fcis-27234	14	2	.	.	X
fcis-27234	14	3	prediction	prediction	NOUN
fcis-27234	14	4	of	of	ADP
fcis-27234	14	5	the	the	DET
fcis-27234	14	6	maximum	maximum	ADJ
fcis-27234	14	7	solar	solar	ADJ
fcis-27234	14	8	value	value	NOUN
fcis-27234	14	9	in	in	ADP
fcis-27234	14	10	the	the	DET
fcis-27234	14	11	solar	solar	ADJ
fcis-27234	14	12	activity	activity	NOUN
fcis-27234	14	13	cycle	cycle	NOUN
fcis-27234	14	14	in	in	ADP
fcis-27234	14	15	order	order	NOUN
fcis-27234	14	16	to	to	PART
fcis-27234	14	17	predict	predict	VERB
fcis-27234	14	18	the	the	DET
fcis-27234	14	19	start	start	ADJ
fcis-27234	14	20	time	time	NOUN
fcis-27234	14	21	and	and	CCONJ
fcis-27234	14	22	duration	duration	NOUN
fcis-27234	14	23	of	of	ADP
fcis-27234	14	24	the	the	DET
fcis-27234	14	25	solar	solar	ADJ
fcis-27234	14	26	maximum	maximum	NOUN
fcis-27234	14	27	in	in	ADP
fcis-27234	14	28	the	the	DET
fcis-27234	14	29	next	next	ADJ
fcis-27234	14	30	solar	solar	ADJ
fcis-27234	14	31	activity	activity	NOUN
fcis-27234	14	32	cycle	cycle	NOUN
fcis-27234	14	33	,	,	PUNCT
fcis-27234	14	34	pearson	pearson	PROPN
fcis-27234	14	35	's	's	PART
fcis-27234	14	36	correlation	correlation	NOUN
fcis-27234	14	37	coefficient	coefficient	NOUN
fcis-27234	14	38	detects	detect	NOUN
fcis-27234	14	39	that	that	SCONJ
fcis-27234	14	40	there	there	PRON
fcis-27234	14	41	is	be	VERB
fcis-27234	14	42	a	a	DET
fcis-27234	14	43	correlation	correlation	NOUN
fcis-27234	14	44	between	between	ADP
fcis-27234	14	45	the	the	DET
fcis-27234	14	46	maximum	maximum	NOUN
fcis-27234	14	47	and	and	CCONJ
fcis-27234	14	48	the	the	DET
fcis-27234	14	49	solar	solar	ADJ
fcis-27234	14	50	black	black	ADJ
fcis-27234	14	51	number	number	NOUN
fcis-27234	14	52	.	.	PUNCT
fcis-27234	15	1	it	it	PRON
fcis-27234	15	2	can	can	AUX
fcis-27234	15	3	be	be	AUX
fcis-27234	15	4	seen	see	VERB
fcis-27234	15	5	that	that	SCONJ
fcis-27234	15	6	the	the	DET
fcis-27234	15	7	correlation	correlation	NOUN
fcis-27234	15	8	is	be	AUX
fcis-27234	15	9	strong	strong	ADJ
fcis-27234	15	10	through	through	ADP
fcis-27234	15	11	the	the	DET
fcis-27234	15	12	thermal	thermal	ADJ
fcis-27234	15	13	map	map	NOUN
fcis-27234	15	14	.	.	PUNCT
fcis-27234	16	1	therefore	therefore	ADV
fcis-27234	16	2	,	,	PUNCT
fcis-27234	16	3	a	a	DET
fcis-27234	16	4	single	single	ADJ
fcis-27234	16	5	linear	linear	ADJ
fcis-27234	16	6	regression	regression	NOUN
fcis-27234	16	7	is	be	AUX
fcis-27234	16	8	established	establish	VERB
fcis-27234	16	9	to	to	PART
fcis-27234	16	10	obtain	obtain	VERB
fcis-27234	16	11	the	the	DET
fcis-27234	16	12	negative	negative	ADJ
fcis-27234	16	13	correlation	correlation	NOUN
fcis-27234	16	14	between	between	ADP
fcis-27234	16	15	them	they	PRON
fcis-27234	16	16	before	before	ADV
fcis-27234	16	17	.	.	PUNCT
fcis-27234	17	1	based	base	VERB
fcis-27234	17	2	on	on	ADP
fcis-27234	17	3	adaptive	adaptive	ADJ
fcis-27234	17	4	multivariate	multivariate	NOUN
fcis-27234	17	5	nonlinear	nonlinear	ADJ
fcis-27234	17	6	regression	regression	NOUN
fcis-27234	17	7	-bp	-bp	X
fcis-27234	17	8	neural	neural	ADJ
fcis-27234	17	9	network	network	NOUN
fcis-27234	17	10	model	model	NOUN
fcis-27234	17	11	for	for	ADP
fcis-27234	17	12	solar	solar	ADJ
fcis-27234	17	13	activity	activity	NOUN
fcis-27234	17	14	prediction	prediction	NOUN
fcis-27234	17	15	,	,	PUNCT
fcis-27234	17	16	first	first	ADV
fcis-27234	17	17	establish	establish	VERB
fcis-27234	17	18	the	the	DET
fcis-27234	17	19	relationship	relationship	NOUN
fcis-27234	17	20	between	between	ADP
fcis-27234	17	21	time	time	NOUN
fcis-27234	17	22	and	and	CCONJ
fcis-27234	17	23	the	the	DET
fcis-27234	17	24	number	number	NOUN
fcis-27234	17	25	of	of	ADP
fcis-27234	17	26	sunspots	sunspot	NOUN
fcis-27234	17	27	model	model	NOUN
fcis-27234	17	28	,	,	PUNCT
fcis-27234	17	29	and	and	CCONJ
fcis-27234	17	30	then	then	ADV
fcis-27234	17	31	based	base	VERB
fcis-27234	17	32	on	on	ADP
fcis-27234	17	33	the	the	DET
fcis-27234	17	34	differential	differential	ADJ
fcis-27234	17	35	evolution	evolution	NOUN
fcis-27234	17	36	algorithm	algorithm	NOUN
fcis-27234	17	37	model	model	NOUN
fcis-27234	17	38	for	for	ADP
fcis-27234	17	39	solving	solving	NOUN
fcis-27234	17	40	,	,	PUNCT
fcis-27234	17	41	and	and	CCONJ
fcis-27234	17	42	then	then	ADV
fcis-27234	17	43	use	use	VERB
fcis-27234	17	44	the	the	DET
fcis-27234	17	45	differential	differential	ADJ
fcis-27234	17	46	evolution	evolution	NOUN
fcis-27234	17	47	algorithm	algorithm	NOUN
fcis-27234	17	48	model	model	NOUN
fcis-27234	17	49	and	and	CCONJ
fcis-27234	17	50	bp	bp	PROPN
fcis-27234	17	51	neural	neural	ADJ
fcis-27234	17	52	network	network	NOUN
fcis-27234	17	53	model	model	NOUN
fcis-27234	17	54	for	for	ADP
fcis-27234	17	55	mixed	mixed	ADJ
fcis-27234	17	56	model	model	NOUN
fcis-27234	17	57	to	to	PART
fcis-27234	17	58	solve	solve	VERB
fcis-27234	17	59	,	,	PUNCT
fcis-27234	17	60	calculate	calculate	VERB
fcis-27234	17	61	the	the	DET
fcis-27234	17	62	maximum	maximum	NOUN
fcis-27234	17	63	for	for	ADP
fcis-27234	17	64	back	back	ADJ
fcis-27234	17	65	generation	generation	NOUN
fcis-27234	17	66	,	,	PUNCT
fcis-27234	17	67	to	to	PART
fcis-27234	17	68	achieve	achieve	VERB
fcis-27234	17	69	the	the	DET
fcis-27234	17	70	next	next	ADJ
fcis-27234	17	71	cycle	cycle	NOUN
fcis-27234	17	72	prediction	prediction	NOUN
fcis-27234	17	73	.	.	PUNCT
fcis-27234	18	1	first	first	ADV
fcis-27234	18	2	of	of	ADP
fcis-27234	18	3	all	all	PRON
fcis-27234	18	4	,	,	PUNCT
fcis-27234	18	5	the	the	DET
fcis-27234	18	6	official	official	ADJ
fcis-27234	18	7	data	datum	NOUN
fcis-27234	18	8	was	be	AUX
fcis-27234	18	9	merged	merge	VERB
fcis-27234	18	10	and	and	CCONJ
fcis-27234	18	11	cleaned	clean	VERB
fcis-27234	18	12	,	,	PUNCT
fcis-27234	18	13	and	and	CCONJ
fcis-27234	18	14	the	the	DET
fcis-27234	18	15	historical	historical	ADJ
fcis-27234	18	16	data	datum	NOUN
fcis-27234	18	17	of	of	ADP
fcis-27234	18	18	the	the	DET
fcis-27234	18	19	sunspot	sunspot	NOUN
fcis-27234	18	20	area	area	NOUN
fcis-27234	18	21	was	be	AUX
fcis-27234	18	22	collected	collect	VERB
fcis-27234	18	23	by	by	ADP
fcis-27234	18	24	means	mean	NOUN
fcis-27234	18	25	of	of	ADP
fcis-27234	18	26	python	python	NOUN
fcis-27234	18	27	data	datum	NOUN
fcis-27234	18	28	crawler	crawler	NOUN
fcis-27234	18	29	and	and	CCONJ
fcis-27234	18	30	esa	esa	PROPN
fcis-27234	18	31	.	.	PROPN
fcis-27234	18	32	2.1	2.1	NUM
fcis-27234	18	33	.	.	PUNCT
fcis-27234	19	1	pearson	pearson	PROPN
fcis-27234	19	2	's	's	PART
fcis-27234	19	3	correlation	correlation	NOUN
fcis-27234	19	4	coefficient	coefficient	NOUN
fcis-27234	19	5	by	by	ADP
fcis-27234	19	6	collecting	collect	VERB
fcis-27234	19	7	and	and	CCONJ
fcis-27234	19	8	processing	process	VERB
fcis-27234	19	9	the	the	DET
fcis-27234	19	10	start	start	NOUN
fcis-27234	19	11	time	time	NOUN
fcis-27234	19	12	and	and	CCONJ
fcis-27234	19	13	duration	duration	NOUN
fcis-27234	19	14	of	of	ADP
fcis-27234	19	15	the	the	DET
fcis-27234	19	16	maximum	maximum	ADJ
fcis-27234	19	17	value	value	NOUN
fcis-27234	19	18	of	of	ADP
fcis-27234	19	19	each	each	DET
fcis-27234	19	20	phase	phase	NOUN
fcis-27234	19	21	,	,	PUNCT
fcis-27234	19	22	as	as	SCONJ
fcis-27234	19	23	shown	show	VERB
fcis-27234	19	24	in	in	ADP
fcis-27234	19	25	figure	figure	NOUN
fcis-27234	19	26	1	1	NUM
fcis-27234	19	27	:	:	PUNCT
fcis-27234	19	28	figure	figure	NOUN
fcis-27234	19	29	1	1	NUM
fcis-27234	19	30	.	.	PUNCT
fcis-27234	20	1	the	the	DET
fcis-27234	20	2	start	start	ADJ
fcis-27234	20	3	time	time	NOUN
fcis-27234	20	4	and	and	CCONJ
fcis-27234	20	5	duration	duration	NOUN
fcis-27234	20	6	of	of	ADP
fcis-27234	20	7	the	the	DET
fcis-27234	20	8	maximum	maximum	ADJ
fcis-27234	20	9	value	value	NOUN
fcis-27234	20	10	the	the	DET
fcis-27234	20	11	figure	figure	NOUN
fcis-27234	20	12	shows	show	VERB
fcis-27234	20	13	the	the	DET
fcis-27234	20	14	relationship	relationship	NOUN
fcis-27234	20	15	between	between	ADP
fcis-27234	20	16	the	the	DET
fcis-27234	20	17	solar	solar	ADJ
fcis-27234	20	18	cycle	cycle	NOUN
fcis-27234	20	19	length	length	NOUN
fcis-27234	20	20	and	and	CCONJ
fcis-27234	20	21	the	the	DET
fcis-27234	20	22	maximum	maximum	ADJ
fcis-27234	20	23	duration	duration	NOUN
fcis-27234	20	24	.	.	PUNCT
fcis-27234	21	1	through	through	ADP
fcis-27234	21	2	comparison	comparison	NOUN
fcis-27234	21	3	,	,	PUNCT
fcis-27234	21	4	it	it	PRON
fcis-27234	21	5	can	can	AUX
fcis-27234	21	6	be	be	AUX
fcis-27234	21	7	seen	see	VERB
fcis-27234	21	8	that	that	SCONJ
fcis-27234	21	9	when	when	SCONJ
fcis-27234	21	10	the	the	DET
fcis-27234	21	11	solar	solar	ADJ
fcis-27234	21	12	cycle	cycle	NOUN
fcis-27234	21	13	length	length	NOUN
fcis-27234	21	14	is	be	AUX
fcis-27234	21	15	shorter	short	ADJ
fcis-27234	21	16	,	,	PUNCT
fcis-27234	21	17	the	the	DET
fcis-27234	21	18	maximum	maximum	ADJ
fcis-27234	21	19	duration	duration	NOUN
fcis-27234	21	20	is	be	AUX
fcis-27234	21	21	usually	usually	ADV
fcis-27234	21	22	shorter	short	ADJ
fcis-27234	21	23	.	.	PUNCT
fcis-27234	22	1	when	when	SCONJ
fcis-27234	22	2	the	the	DET
fcis-27234	22	3	solar	solar	ADJ
fcis-27234	22	4	cycle	cycle	NOUN
fcis-27234	22	5	length	length	NOUN
fcis-27234	22	6	is	be	AUX
fcis-27234	22	7	longer	long	ADJ
fcis-27234	22	8	,	,	PUNCT
fcis-27234	22	9	the	the	DET
fcis-27234	22	10	maximum	maximum	ADJ
fcis-27234	22	11	duration	duration	NOUN
fcis-27234	22	12	may	may	AUX
fcis-27234	22	13	reach	reach	VERB
fcis-27234	22	14	a	a	DET
fcis-27234	22	15	relatively	relatively	ADV
fcis-27234	22	16	high	high	ADJ
fcis-27234	22	17	value	value	NOUN
fcis-27234	22	18	.	.	PUNCT
fcis-27234	23	1	it	it	PRON
fcis-27234	23	2	is	be	AUX
fcis-27234	23	3	found	find	VERB
fcis-27234	23	4	that	that	SCONJ
fcis-27234	23	5	there	there	PRON
fcis-27234	23	6	is	be	VERB
fcis-27234	23	7	a	a	DET
fcis-27234	23	8	strong	strong	ADJ
fcis-27234	23	9	relationship	relationship	NOUN
fcis-27234	23	10	51	51	NUM
fcis-27234	23	11	between	between	ADP
fcis-27234	23	12	the	the	DET
fcis-27234	23	13	start	start	ADJ
fcis-27234	23	14	time	time	NOUN
fcis-27234	23	15	and	and	CCONJ
fcis-27234	23	16	the	the	DET
fcis-27234	23	17	duration	duration	NOUN
fcis-27234	23	18	of	of	ADP
fcis-27234	23	19	the	the	DET
fcis-27234	23	20	maximum	maximum	NOUN
fcis-27234	23	21	of	of	ADP
fcis-27234	23	22	each	each	DET
fcis-27234	23	23	period	period	NOUN
fcis-27234	23	24	.	.	PUNCT
fcis-27234	24	1	therefore	therefore	ADV
fcis-27234	24	2	,	,	PUNCT
fcis-27234	24	3	in	in	ADP
fcis-27234	24	4	order	order	NOUN
fcis-27234	24	5	to	to	PART
fcis-27234	24	6	highlight	highlight	VERB
fcis-27234	24	7	the	the	DET
fcis-27234	24	8	significance	significance	NOUN
fcis-27234	24	9	of	of	ADP
fcis-27234	24	10	the	the	DET
fcis-27234	24	11	relationship	relationship	NOUN
fcis-27234	24	12	,	,	PUNCT
fcis-27234	24	13	pearson	pearson	PROPN
fcis-27234	24	14	's	's	PART
fcis-27234	24	15	correlation	correlation	NOUN
fcis-27234	24	16	coefficient	coefficient	NOUN
fcis-27234	24	17	is	be	AUX
fcis-27234	24	18	introduced	introduce	VERB
fcis-27234	24	19	.	.	PUNCT
fcis-27234	25	1	the	the	DET
fcis-27234	25	2	pearson	pearson	PROPN
fcis-27234	25	3	correlation	correlation	NOUN
fcis-27234	25	4	coefficient	coefficient	NOUN
fcis-27234	25	5	applies	apply	VERB
fcis-27234	25	6	to	to	ADP
fcis-27234	25	7	linear	linear	ADJ
fcis-27234	25	8	correlation	correlation	NOUN
fcis-27234	25	9	relationships	relationship	NOUN
fcis-27234	25	10	.	.	PUNCT
fcis-27234	26	1	the	the	DET
fcis-27234	26	2	degree	degree	NOUN
fcis-27234	26	3	of	of	ADP
fcis-27234	26	4	correlation	correlation	NOUN
fcis-27234	26	5	is	be	AUX
fcis-27234	26	6	related	relate	VERB
fcis-27234	26	7	to	to	ADP
fcis-27234	26	8	the	the	DET
fcis-27234	26	9	p	p	NOUN
fcis-27234	26	10	-	-	PUNCT
fcis-27234	26	11	value	value	NOUN
fcis-27234	26	12	,	,	PUNCT
fcis-27234	26	13	which	which	PRON
fcis-27234	26	14	is	be	AUX
fcis-27234	26	15	its	its	PRON
fcis-27234	26	16	correlation	correlation	NOUN
fcis-27234	26	17	coefficient	coefficient	NOUN
fcis-27234	26	18	.	.	PUNCT
fcis-27234	27	1	the	the	PRON
fcis-27234	27	2	closer	close	ADV
fcis-27234	27	3	the	the	DET
fcis-27234	27	4	correlation	correlation	NOUN
fcis-27234	27	5	coefficient	coefficient	NOUN
fcis-27234	27	6	is	be	AUX
fcis-27234	27	7	to	to	ADP
fcis-27234	27	8	1	1	NUM
fcis-27234	27	9	,	,	PUNCT
fcis-27234	27	10	the	the	PRON
fcis-27234	27	11	stronger	strong	ADJ
fcis-27234	27	12	the	the	DET
fcis-27234	27	13	correlation	correlation	NOUN
fcis-27234	27	14	between	between	ADP
fcis-27234	27	15	the	the	DET
fcis-27234	27	16	two	two	NUM
fcis-27234	27	17	factors	factor	NOUN
fcis-27234	27	18	is	be	AUX
fcis-27234	27	19	said	say	VERB
fcis-27234	27	20	to	to	PART
fcis-27234	27	21	be	be	AUX
fcis-27234	27	22	;	;	PUNCT
fcis-27234	27	23	on	on	ADP
fcis-27234	27	24	the	the	DET
fcis-27234	27	25	contrary	contrary	NOUN
fcis-27234	27	26	,	,	PUNCT
fcis-27234	27	27	the	the	PRON
fcis-27234	27	28	closer	close	ADV
fcis-27234	27	29	the	the	DET
fcis-27234	27	30	correlation	correlation	NOUN
fcis-27234	27	31	coefficient	coefficient	NOUN
fcis-27234	27	32	goes	go	VERB
fcis-27234	27	33	to	to	ADP
fcis-27234	27	34	0	0	NUM
fcis-27234	27	35	,	,	PUNCT
fcis-27234	27	36	the	the	PRON
fcis-27234	27	37	weaker	weak	ADJ
fcis-27234	27	38	the	the	DET
fcis-27234	27	39	correlation	correlation	NOUN
fcis-27234	27	40	.	.	PUNCT
fcis-27234	28	1	the	the	DET
fcis-27234	28	2	pearson	pearson	PROPN
fcis-27234	28	3	correlation	correlation	NOUN
fcis-27234	28	4	coefficient	coefficient	NOUN
fcis-27234	28	5	is	be	AUX
fcis-27234	28	6	often	often	ADV
fcis-27234	28	7	used	use	VERB
fcis-27234	28	8	to	to	PART
fcis-27234	28	9	measure	measure	VERB
fcis-27234	28	10	the	the	DET
fcis-27234	28	11	degree	degree	NOUN
fcis-27234	28	12	of	of	ADP
fcis-27234	28	13	correlation	correlation	NOUN
fcis-27234	28	14	(	(	PUNCT
fcis-27234	28	15	linear	linear	ADJ
fcis-27234	28	16	correlation	correlation	NOUN
fcis-27234	28	17	)	)	PUNCT
fcis-27234	28	18	between	between	ADP
fcis-27234	28	19	two	two	NUM
fcis-27234	28	20	variables	variable	NOUN
fcis-27234	28	21	x	x	PUNCT
fcis-27234	28	22	and	and	CCONJ
fcis-27234	28	23	y	y	PROPN
fcis-27234	28	24	,	,	PUNCT
fcis-27234	28	25	with	with	ADP
fcis-27234	28	26	values	value	NOUN
fcis-27234	28	27	between	between	ADP
fcis-27234	28	28	-1	-1	PUNCT
fcis-27234	28	29	and	and	CCONJ
fcis-27234	28	30	1	1	X
fcis-27234	28	31	.	.	X
fcis-27234	28	32	introduce	introduce	VERB
fcis-27234	28	33	covariance	covariance	NOUN
fcis-27234	28	34	and	and	CCONJ
fcis-27234	28	35	variance	variance	NOUN
fcis-27234	28	36	here	here	ADV
fcis-27234	28	37	:	:	PUNCT
fcis-27234	28	38	the	the	DET
fcis-27234	28	39	pearson	pearson	PROPN
fcis-27234	28	40	correlation	correlation	NOUN
fcis-27234	28	41	coefficient	coefficient	NOUN
fcis-27234	28	42	between	between	ADP
fcis-27234	28	43	two	two	NUM
fcis-27234	28	44	variables	variable	NOUN
fcis-27234	28	45	is	be	AUX
fcis-27234	28	46	defined	define	VERB
fcis-27234	28	47	as	as	ADP
fcis-27234	28	48	the	the	DET
fcis-27234	28	49	quotient	quotient	NOUN
fcis-27234	28	50	of	of	ADP
fcis-27234	28	51	the	the	DET
fcis-27234	28	52	covariance	covariance	NOUN
fcis-27234	28	53	and	and	CCONJ
fcis-27234	28	54	standard	standard	ADJ
fcis-27234	28	55	deviation	deviation	NOUN
fcis-27234	28	56	between	between	ADP
fcis-27234	28	57	the	the	DET
fcis-27234	28	58	two	two	NUM
fcis-27234	28	59	variables	variable	NOUN
fcis-27234	28	60	.	.	PUNCT
fcis-27234	29	1			NOUN
fcis-27234	29	2			PUNCT
fcis-27234	29	3			PROPN
fcis-27234	29	4			NOUN
fcis-27234	29	5	x	x	PROPN
fcis-27234	29	6	y	y	PROPN
fcis-27234	29	7	,	,	PUNCT
fcis-27234	29	8	e	e	X
fcis-27234	29	9	x	x	SYM
fcis-27234	29	10	μ	μ	NUM
fcis-27234	29	11	y	y	PROPN
fcis-27234	29	12	μcov	μcov	NOUN
fcis-27234	29	13	x	x	NOUN
fcis-27234	29	14	,	,	PUNCT
fcis-27234	29	15	y	y	PROPN
fcis-27234	29	16	x	x	X
fcis-27234	29	17	y	y	NOUN
fcis-27234	29	18	x	x	PUNCT
fcis-27234	29	19	y	y	NOUN
fcis-27234	29	20	x	x	SYM
fcis-27234	29	21	y	y	PROPN
fcis-27234	29	22			ADP
fcis-27234	29	23			PROPN
fcis-27234	29	24			X
fcis-27234	29	25			X
fcis-27234	29	26			X
fcis-27234	29	27			PROPN
fcis-27234	29	28			PROPN
fcis-27234	29	29			NOUN
fcis-27234	29	30			PROPN
fcis-27234	29	31			PROPN
fcis-27234	29	32	(	(	PUNCT
fcis-27234	29	33	1	1	NUM
fcis-27234	29	34	)	)	PUNCT
fcis-27234	29	35	establish	establish	VERB
fcis-27234	29	36	a	a	DET
fcis-27234	29	37	heat	heat	NOUN
fcis-27234	29	38	map	map	NOUN
fcis-27234	29	39	to	to	PART
fcis-27234	29	40	further	far	ADV
fcis-27234	29	41	observe	observe	VERB
fcis-27234	29	42	its	its	PRON
fcis-27234	29	43	significance	significance	NOUN
fcis-27234	29	44	.	.	PUNCT
fcis-27234	30	1	figure	figure	NOUN
fcis-27234	30	2	2	2	NUM
fcis-27234	30	3	.	.	PUNCT
fcis-27234	30	4	duration	duration	NOUN
fcis-27234	30	5	and	and	CCONJ
fcis-27234	30	6	solar	solar	ADJ
fcis-27234	30	7	cycle	cycle	NOUN
fcis-27234	30	8	duration	duration	NOUN
fcis-27234	30	9	heat	heat	NOUN
fcis-27234	30	10	map	map	NOUN
fcis-27234	30	11	as	as	SCONJ
fcis-27234	30	12	can	can	AUX
fcis-27234	30	13	be	be	AUX
fcis-27234	30	14	seen	see	VERB
fcis-27234	30	15	in	in	ADP
fcis-27234	30	16	figure	figure	NOUN
fcis-27234	30	17	2	2	NUM
fcis-27234	30	18	:	:	PUNCT
fcis-27234	30	19	duration	duration	NOUN
fcis-27234	30	20	has	have	VERB
fcis-27234	30	21	a	a	DET
fcis-27234	30	22	moderate	moderate	ADJ
fcis-27234	30	23	positive	positive	ADJ
fcis-27234	30	24	correlation	correlation	NOUN
fcis-27234	30	25	with	with	ADP
fcis-27234	30	26	solar	solar	ADJ
fcis-27234	30	27	cycle	cycle	NOUN
fcis-27234	30	28	duration	duration	NOUN
fcis-27234	30	29	,	,	PUNCT
fcis-27234	30	30	a	a	DET
fcis-27234	30	31	strong	strong	ADJ
fcis-27234	30	32	negative	negative	ADJ
fcis-27234	30	33	correlation	correlation	NOUN
fcis-27234	30	34	with	with	ADP
fcis-27234	30	35	the	the	DET
fcis-27234	30	36	maximum	maximum	ADJ
fcis-27234	30	37	value	value	NOUN
fcis-27234	30	38	of	of	ADP
fcis-27234	30	39	the	the	DET
fcis-27234	30	40	number	number	NOUN
fcis-27234	30	41	of	of	ADP
fcis-27234	30	42	black	black	ADJ
fcis-27234	30	43	characters	character	NOUN
fcis-27234	30	44	,	,	PUNCT
fcis-27234	30	45	and	and	CCONJ
fcis-27234	30	46	a	a	DET
fcis-27234	30	47	moderate	moderate	ADJ
fcis-27234	30	48	negative	negative	ADJ
fcis-27234	30	49	correlation	correlation	NOUN
fcis-27234	30	50	with	with	ADP
fcis-27234	30	51	the	the	DET
fcis-27234	30	52	minimum	minimum	ADJ
fcis-27234	30	53	value	value	NOUN
fcis-27234	30	54	of	of	ADP
fcis-27234	30	55	the	the	DET
fcis-27234	30	56	number	number	NOUN
fcis-27234	30	57	of	of	ADP
fcis-27234	30	58	black	black	ADJ
fcis-27234	30	59	characters	character	NOUN
fcis-27234	30	60	.	.	PUNCT
fcis-27234	31	1	2.2	2.2	NUM
fcis-27234	31	2	.	.	PUNCT
fcis-27234	32	1	unary	unary	ADJ
fcis-27234	32	2	linear	linear	PROPN
fcis-27234	32	3	regression	regression	NOUN
fcis-27234	32	4	it	it	PRON
fcis-27234	32	5	can	can	AUX
fcis-27234	32	6	be	be	AUX
fcis-27234	32	7	seen	see	VERB
fcis-27234	32	8	from	from	ADP
fcis-27234	32	9	the	the	DET
fcis-27234	32	10	above	above	ADV
fcis-27234	32	11	that	that	SCONJ
fcis-27234	32	12	the	the	DET
fcis-27234	32	13	maximum	maximum	ADJ
fcis-27234	32	14	number	number	NOUN
fcis-27234	32	15	of	of	ADP
fcis-27234	32	16	sunspots	sunspot	NOUN
fcis-27234	32	17	shows	show	VERB
fcis-27234	32	18	a	a	DET
fcis-27234	32	19	strong	strong	ADJ
fcis-27234	32	20	negative	negative	ADJ
fcis-27234	32	21	correlation	correlation	NOUN
fcis-27234	32	22	with	with	ADP
fcis-27234	32	23	the	the	DET
fcis-27234	32	24	duration	duration	NOUN
fcis-27234	32	25	,	,	PUNCT
fcis-27234	32	26	so	so	CCONJ
fcis-27234	32	27	the	the	DET
fcis-27234	32	28	correlation	correlation	NOUN
fcis-27234	32	29	expression	expression	NOUN
fcis-27234	32	30	is	be	AUX
fcis-27234	32	31	based	base	VERB
fcis-27234	32	32	on	on	ADP
fcis-27234	32	33	the	the	DET
fcis-27234	32	34	single	single	ADJ
fcis-27234	32	35	linear	linear	PROPN
fcis-27234	32	36	regression	regression	NOUN
fcis-27234	32	37	.	.	PUNCT
fcis-27234	33	1	given	give	VERB
fcis-27234	33	2	that	that	DET
fcis-27234	33	3	p<0.05	p<0.05	NOUN
fcis-27234	33	4	,	,	PUNCT
fcis-27234	33	5	the	the	DET
fcis-27234	33	6	regression	regression	NOUN
fcis-27234	33	7	model	model	NOUN
fcis-27234	33	8	y=6.9871	y=6.9871	PROPN
fcis-27234	33	9	-	-	PUNCT
fcis-27234	33	10	0.0147x	0.0147x	PROPN
fcis-27234	33	11	was	be	AUX
fcis-27234	33	12	established	establish	VERB
fcis-27234	33	13	.	.	PUNCT
fcis-27234	34	1	2.3	2.3	NUM
fcis-27234	34	2	.	.	PUNCT
fcis-27234	34	3	establishment	establishment	NOUN
fcis-27234	34	4	of	of	ADP
fcis-27234	34	5	the	the	DET
fcis-27234	34	6	relationship	relationship	NOUN
fcis-27234	34	7	model	model	NOUN
fcis-27234	34	8	between	between	ADP
fcis-27234	34	9	time	time	NOUN
fcis-27234	34	10	and	and	CCONJ
fcis-27234	34	11	sunspot	sunspot	NOUN
fcis-27234	34	12	number	number	NOUN
fcis-27234	34	13	we	we	PRON
fcis-27234	34	14	need	need	VERB
fcis-27234	34	15	to	to	PART
fcis-27234	34	16	build	build	VERB
fcis-27234	34	17	a	a	DET
fcis-27234	34	18	regression	regression	NOUN
fcis-27234	34	19	analysis	analysis	NOUN
fcis-27234	34	20	model	model	NOUN
fcis-27234	34	21	to	to	PART
fcis-27234	34	22	quantify	quantify	VERB
fcis-27234	34	23	the	the	DET
fcis-27234	34	24	relationship	relationship	NOUN
fcis-27234	34	25	between	between	ADP
fcis-27234	34	26	time	time	NOUN
fcis-27234	34	27	and	and	CCONJ
fcis-27234	34	28	the	the	DET
fcis-27234	34	29	number	number	NOUN
fcis-27234	34	30	of	of	ADP
fcis-27234	34	31	sunspots	sunspot	NOUN
fcis-27234	34	32	.	.	PUNCT
fcis-27234	35	1	in	in	ADP
fcis-27234	35	2	regression	regression	NOUN
fcis-27234	35	3	analysis	analysis	NOUN
fcis-27234	35	4	,	,	PUNCT
fcis-27234	35	5	we	we	PRON
fcis-27234	35	6	usually	usually	ADV
fcis-27234	35	7	use	use	VERB
fcis-27234	35	8	independent	independent	ADJ
fcis-27234	35	9	variables	variable	NOUN
fcis-27234	35	10	(	(	PUNCT
fcis-27234	35	11	time	time	NOUN
fcis-27234	35	12	)	)	PUNCT
fcis-27234	35	13	and	and	CCONJ
fcis-27234	35	14	dependent	dependent	ADJ
fcis-27234	35	15	variables	variable	NOUN
fcis-27234	35	16	(	(	PUNCT
fcis-27234	35	17	number	number	NOUN
fcis-27234	35	18	of	of	ADP
fcis-27234	35	19	sunspots	sunspot	NOUN
fcis-27234	35	20	)	)	PUNCT
fcis-27234	35	21	to	to	PART
fcis-27234	35	22	build	build	VERB
fcis-27234	35	23	models	model	NOUN
fcis-27234	35	24	.	.	PUNCT
fcis-27234	36	1	by	by	ADP
fcis-27234	36	2	fitting	fit	VERB
fcis-27234	36	3	this	this	DET
fcis-27234	36	4	data	datum	NOUN
fcis-27234	36	5	,	,	PUNCT
fcis-27234	36	6	we	we	PRON
fcis-27234	36	7	can	can	AUX
fcis-27234	36	8	get	get	VERB
fcis-27234	36	9	a	a	DET
fcis-27234	36	10	regression	regression	NOUN
fcis-27234	36	11	equation	equation	NOUN
fcis-27234	36	12	that	that	PRON
fcis-27234	36	13	describes	describe	VERB
fcis-27234	36	14	the	the	DET
fcis-27234	36	15	relationship	relationship	NOUN
fcis-27234	36	16	between	between	ADP
fcis-27234	36	17	time	time	NOUN
fcis-27234	36	18	and	and	CCONJ
fcis-27234	36	19	the	the	DET
fcis-27234	36	20	number	number	NOUN
fcis-27234	36	21	of	of	ADP
fcis-27234	36	22	sunspots	sunspot	NOUN
fcis-27234	36	23	.	.	PUNCT
fcis-27234	37	1	this	this	DET
fcis-27234	37	2	equation	equation	NOUN
fcis-27234	37	3	can	can	AUX
fcis-27234	37	4	be	be	AUX
fcis-27234	37	5	used	use	VERB
fcis-27234	37	6	to	to	PART
fcis-27234	37	7	predict	predict	VERB
fcis-27234	37	8	future	future	ADJ
fcis-27234	37	9	trends	trend	NOUN
fcis-27234	37	10	in	in	ADP
fcis-27234	37	11	sunspot	sunspot	NOUN
fcis-27234	37	12	numbers	number	NOUN
fcis-27234	37	13	.	.	PUNCT
fcis-27234	38	1	the	the	DET
fcis-27234	38	2	relationship	relationship	NOUN
fcis-27234	38	3	can	can	AUX
fcis-27234	38	4	be	be	AUX
fcis-27234	38	5	expressed	express	VERB
fcis-27234	38	6	as	as	ADP
fcis-27234	38	7	:	:	PUNCT
fcis-27234	38	8			NOUN
fcis-27234	38	9			PROPN
fcis-27234	38	10			NOUN
fcis-27234	39	1	i	i	PROPN
fcis-27234	40	1	i	i	PRON
fcis-27234	40	2	i	i	PRON
fcis-27234	40	3	i	i	VERB
fcis-27234	40	4	1	1	NUM
fcis-27234	40	5	2	2	NUM
fcis-27234	40	6	j	j	NOUN
fcis-27234	40	7	1	1	NUM
fcis-27234	40	8	2	2	NUM
fcis-27234	40	9	p	p	NOUN
fcis-27234	40	10	iy	iy	X
fcis-27234	40	11	f	f	X
fcis-27234	40	12	x	x	INTJ
fcis-27234	40	13	,	,	PUNCT
fcis-27234	40	14	x	x	X
fcis-27234	40	15	,	,	PUNCT
fcis-27234	40	16	,	,	PUNCT
fcis-27234	40	17	x	x	X
fcis-27234	40	18	,	,	PUNCT
fcis-27234	40	19	θ	θ	PROPN
fcis-27234	40	20	,	,	PUNCT
fcis-27234	40	21	θ	θ	PROPN
fcis-27234	40	22	,	,	PUNCT
fcis-27234	40	23	,	,	PUNCT
fcis-27234	40	24	θ	θ	PROPN
fcis-27234	40	25	σ	σ	PROPN
fcis-27234	40	26	ε	ε	PROPN
fcis-27234	40	27	i	i	PRON
fcis-27234	40	28	1	1	NUM
fcis-27234	40	29	,	,	PUNCT
fcis-27234	40	30	2	2	NUM
fcis-27234	40	31	,	,	PUNCT
fcis-27234	40	32	,	,	PUNCT
fcis-27234	40	33	n	n	PROPN
fcis-27234	40	34			ADJ
fcis-27234	40	35			NOUN
fcis-27234	40	36			NOUN
fcis-27234	40	37			NOUN
fcis-27234	40	38	(	(	PUNCT
fcis-27234	40	39	2	2	NUM
fcis-27234	40	40	)	)	PUNCT
fcis-27234	40	41	where	where	SCONJ
fcis-27234	40	42	,	,	PUNCT
fcis-27234	40	43	is	be	AUX
fcis-27234	40	44	the	the	DET
fcis-27234	40	45	true	true	ADJ
fcis-27234	40	46	value	value	NOUN
fcis-27234	40	47	;	;	PUNCT
fcis-27234	40	48	represents	represent	VERB
fcis-27234	40	49	group	group	NOUN
fcis-27234	40	50	data	datum	NOUN
fcis-27234	40	51	.	.	PUNCT
fcis-27234	41	1	f	f	X
fcis-27234	42	1	x	x	SYM
fcis-27234	42	2	,	,	PUNCT
fcis-27234	42	3	x	x	SYM
fcis-27234	42	4	,	,	PUNCT
fcis-27234	42	5	⋯	⋯	PROPN
fcis-27234	42	6	,	,	PUNCT
fcis-27234	42	7	x	x	X
fcis-27234	42	8	,	,	PUNCT
fcis-27234	42	9	θ	θ	PROPN
fcis-27234	42	10	,	,	PUNCT
fcis-27234	42	11	θ	θ	PROPN
fcis-27234	42	12	,	,	PUNCT
fcis-27234	42	13	⋯	⋯	PROPN
fcis-27234	42	14	,	,	PUNCT
fcis-27234	42	15	θ	θ	PROPN
fcis-27234	42	16	is	be	AUX
fcis-27234	42	17	a	a	DET
fcis-27234	42	18	multivariate	multivariate	NOUN
fcis-27234	42	19	nonlinear	nonlinear	ADJ
fcis-27234	42	20	function	function	NOUN
fcis-27234	42	21	,	,	PUNCT
fcis-27234	42	22	representing	represent	VERB
fcis-27234	42	23	the	the	DET
fcis-27234	42	24	deterministic	deterministic	ADJ
fcis-27234	42	25	part	part	NOUN
fcis-27234	42	26	.	.	PUNCT
fcis-27234	43	1	x	x	X
fcis-27234	43	2	,	,	PUNCT
fcis-27234	43	3	x	x	X
fcis-27234	43	4	,	,	PUNCT
fcis-27234	43	5	⋯	⋯	PROPN
fcis-27234	43	6	,	,	PUNCT
fcis-27234	43	7	x	x	X
fcis-27234	43	8	is	be	AUX
fcis-27234	43	9	the	the	DET
fcis-27234	43	10	independent	independent	ADJ
fcis-27234	43	11	variable	variable	NOUN
fcis-27234	43	12	;	;	PUNCT
fcis-27234	43	13	θ	θ	PROPN
fcis-27234	43	14	,	,	PUNCT
fcis-27234	43	15	θ	θ	PROPN
fcis-27234	43	16	,	,	PUNCT
fcis-27234	43	17	⋯	⋯	PROPN
fcis-27234	43	18	,	,	PUNCT
fcis-27234	43	19	θ	θ	PROPN
fcis-27234	43	20	is	be	AUX
fcis-27234	43	21	an	an	DET
fcis-27234	43	22	unknown	unknown	ADJ
fcis-27234	43	23	model	model	NOUN
fcis-27234	43	24	parameter	parameter	NOUN
fcis-27234	43	25	of	of	ADP
fcis-27234	43	26	a	a	DET
fcis-27234	43	27	multivariate	multivariate	NOUN
fcis-27234	43	28	nonlinear	nonlinear	ADJ
fcis-27234	43	29	function	function	NOUN
fcis-27234	43	30	.	.	PUNCT
fcis-27234	44	1	σ	σ	NOUN
fcis-27234	44	2	ε	ε	PROPN
fcis-27234	44	3	is	be	AUX
fcis-27234	44	4	the	the	DET
fcis-27234	44	5	random	random	ADJ
fcis-27234	44	6	part	part	NOUN
fcis-27234	44	7	,	,	PUNCT
fcis-27234	44	8	ε	ε	PROPN
fcis-27234	44	9	is	be	AUX
fcis-27234	44	10	a	a	DET
fcis-27234	44	11	random	random	ADJ
fcis-27234	44	12	variable	variable	NOUN
fcis-27234	44	13	that	that	PRON
fcis-27234	44	14	follows	follow	VERB
fcis-27234	44	15	the	the	DET
fcis-27234	44	16	n	n	PROPN
fcis-27234	44	17	(	(	PUNCT
fcis-27234	44	18	0,1	0,1	NUM
fcis-27234	44	19	)	)	PUNCT
fcis-27234	44	20	distribution	distribution	NOUN
fcis-27234	44	21	.	.	PUNCT
fcis-27234	45	1	σ	σ	NOUN
fcis-27234	45	2	is	be	AUX
fcis-27234	45	3	the	the	DET
fcis-27234	45	4	random	random	ADJ
fcis-27234	45	5	distribution	distribution	NOUN
fcis-27234	45	6	standard	standard	NOUN
fcis-27234	45	7	deviation	deviation	NOUN
fcis-27234	45	8	of	of	ADP
fcis-27234	45	9	group	group	NOUN
fcis-27234	45	10	data	datum	NOUN
fcis-27234	45	11	.	.	PUNCT
fcis-27234	46	1	at	at	ADP
fcis-27234	46	2	present	present	ADJ
fcis-27234	46	3	,	,	PUNCT
fcis-27234	46	4	the	the	DET
fcis-27234	46	5	selection	selection	NOUN
fcis-27234	46	6	of	of	ADP
fcis-27234	46	7	nonlinear	nonlinear	ADJ
fcis-27234	46	8	regression	regression	NOUN
fcis-27234	46	9	models	model	NOUN
fcis-27234	46	10	mainly	mainly	ADV
fcis-27234	46	11	relies	rely	VERB
fcis-27234	46	12	on	on	ADP
fcis-27234	46	13	empirical	empirical	ADJ
fcis-27234	46	14	or	or	CCONJ
fcis-27234	46	15	experimental	experimental	ADJ
fcis-27234	46	16	methods	method	NOUN
fcis-27234	46	17	[	[	X
fcis-27234	46	18	6	6	NUM
fcis-27234	46	19	]	]	PUNCT
fcis-27234	46	20	.	.	PUNCT
fcis-27234	47	1	in	in	ADP
fcis-27234	47	2	order	order	NOUN
fcis-27234	47	3	to	to	PART
fcis-27234	47	4	solve	solve	VERB
fcis-27234	47	5	the	the	DET
fcis-27234	47	6	problem	problem	NOUN
fcis-27234	47	7	of	of	ADP
fcis-27234	47	8	greater	great	ADJ
fcis-27234	47	9	error	error	NOUN
fcis-27234	47	10	in	in	ADP
fcis-27234	47	11	the	the	DET
fcis-27234	47	12	empirical	empirical	ADJ
fcis-27234	47	13	method	method	NOUN
fcis-27234	47	14	,	,	PUNCT
fcis-27234	47	15	we	we	PRON
fcis-27234	47	16	can	can	AUX
fcis-27234	47	17	use	use	VERB
fcis-27234	47	18	the	the	DET
fcis-27234	47	19	experimental	experimental	ADJ
fcis-27234	47	20	method	method	NOUN
fcis-27234	47	21	to	to	PART
fcis-27234	47	22	determine	determine	VERB
fcis-27234	47	23	the	the	DET
fcis-27234	47	24	regression	regression	NOUN
fcis-27234	47	25	model	model	NOUN
fcis-27234	47	26	suitable	suitable	ADJ
fcis-27234	47	27	for	for	ADP
fcis-27234	47	28	describing	describe	VERB
fcis-27234	47	29	the	the	DET
fcis-27234	47	30	relationship	relationship	NOUN
fcis-27234	47	31	between	between	ADP
fcis-27234	47	32	time	time	NOUN
fcis-27234	47	33	and	and	CCONJ
fcis-27234	47	34	the	the	DET
fcis-27234	47	35	number	number	NOUN
fcis-27234	47	36	of	of	ADP
fcis-27234	47	37	sunspots	sunspot	NOUN
fcis-27234	47	38	.	.	PUNCT
fcis-27234	48	1	when	when	SCONJ
fcis-27234	48	2	choosing	choose	VERB
fcis-27234	48	3	a	a	DET
fcis-27234	48	4	regression	regression	NOUN
fcis-27234	48	5	model	model	NOUN
fcis-27234	48	6	,	,	PUNCT
fcis-27234	48	7	we	we	PRON
fcis-27234	48	8	can	can	AUX
fcis-27234	48	9	find	find	VERB
fcis-27234	48	10	the	the	DET
fcis-27234	48	11	most	most	ADV
fcis-27234	48	12	suitable	suitable	ADJ
fcis-27234	48	13	one	one	NUM
fcis-27234	48	14	by	by	ADP
fcis-27234	48	15	comparing	compare	VERB
fcis-27234	48	16	different	different	ADJ
fcis-27234	48	17	nonlinear	nonlinear	ADJ
fcis-27234	48	18	regression	regression	NOUN
fcis-27234	48	19	models	model	NOUN
fcis-27234	48	20	.	.	PUNCT
fcis-27234	49	1	through	through	ADP
fcis-27234	49	2	this	this	DET
fcis-27234	49	3	series	series	NOUN
fcis-27234	49	4	of	of	ADP
fcis-27234	49	5	experimental	experimental	ADJ
fcis-27234	49	6	steps	step	NOUN
fcis-27234	49	7	,	,	PUNCT
fcis-27234	49	8	we	we	PRON
fcis-27234	49	9	can	can	AUX
fcis-27234	49	10	more	more	ADV
fcis-27234	49	11	accurately	accurately	ADV
fcis-27234	49	12	determine	determine	VERB
fcis-27234	49	13	the	the	DET
fcis-27234	49	14	regression	regression	NOUN
fcis-27234	49	15	model	model	NOUN
fcis-27234	49	16	that	that	PRON
fcis-27234	49	17	is	be	AUX
fcis-27234	49	18	suitable	suitable	ADJ
fcis-27234	49	19	to	to	PART
fcis-27234	49	20	describe	describe	VERB
fcis-27234	49	21	the	the	DET
fcis-27234	49	22	relationship	relationship	NOUN
fcis-27234	49	23	between	between	ADP
fcis-27234	49	24	time	time	NOUN
fcis-27234	49	25	and	and	CCONJ
fcis-27234	49	26	the	the	DET
fcis-27234	49	27	number	number	NOUN
fcis-27234	49	28	of	of	ADP
fcis-27234	49	29	sunspots	sunspot	NOUN
fcis-27234	49	30	,	,	PUNCT
fcis-27234	49	31	and	and	CCONJ
fcis-27234	49	32	obtain	obtain	VERB
fcis-27234	49	33	more	more	ADV
fcis-27234	49	34	accurate	accurate	ADJ
fcis-27234	49	35	results	result	NOUN
fcis-27234	49	36	.	.	PUNCT
fcis-27234	50	1	therefore	therefore	ADV
fcis-27234	50	2	,	,	PUNCT
fcis-27234	50	3	in	in	ADP
fcis-27234	50	4	this	this	DET
fcis-27234	50	5	paper	paper	NOUN
fcis-27234	50	6	,	,	PUNCT
fcis-27234	50	7	the	the	DET
fcis-27234	50	8	regression	regression	NOUN
fcis-27234	50	9	model	model	NOUN
fcis-27234	50	10	suitable	suitable	ADJ
fcis-27234	50	11	for	for	ADP
fcis-27234	50	12	this	this	DET
fcis-27234	50	13	relationship	relationship	NOUN
fcis-27234	50	14	is	be	AUX
fcis-27234	50	15	determined	determine	VERB
fcis-27234	50	16	by	by	ADP
fcis-27234	50	17	experimental	experimental	ADJ
fcis-27234	50	18	method	method	NOUN
fcis-27234	50	19	as	as	SCONJ
fcis-27234	50	20	follows	follow	VERB
fcis-27234	50	21	:	:	PUNCT
fcis-27234	50	22			NOUN
fcis-27234	50	23			PUNCT
fcis-27234	50	24	1	1	NUM
fcis-27234	50	25	*	*	SYM
fcis-27234	50	26	2	2	NUM
fcis-27234	50	27	*	*	SYM
fcis-27234	50	28	3	3	NUM
fcis-27234	50	29	4y	4y	NOUN
fcis-27234	50	30	x	x	X
fcis-27234	50	31	sin	sin	NOUN
fcis-27234	50	32	x	x	X
fcis-27234	50	33	x	x	SYM
fcis-27234	50	34	x	x	X
fcis-27234	50	35	x	x	PROPN
fcis-27234	50	36			PUNCT
fcis-27234	50	37			X
fcis-27234	50	38	(	(	PUNCT
fcis-27234	50	39	3	3	NUM
fcis-27234	50	40	)	)	PUNCT
fcis-27234	50	41	2.4	2.4	NUM
fcis-27234	50	42	.	.	PUNCT
fcis-27234	51	1	model	model	NOUN
fcis-27234	51	2	solving	solving	NOUN
fcis-27234	51	3	based	base	VERB
fcis-27234	51	4	on	on	ADP
fcis-27234	51	5	differential	differential	ADJ
fcis-27234	51	6	evolution	evolution	NOUN
fcis-27234	51	7	algorithm	algorithm	NOUN
fcis-27234	51	8	we	we	PRON
fcis-27234	51	9	are	be	AUX
fcis-27234	51	10	faced	face	VERB
fcis-27234	51	11	with	with	ADP
fcis-27234	51	12	a	a	DET
fcis-27234	51	13	multi	multi	ADJ
fcis-27234	51	14	-	-	ADJ
fcis-27234	51	15	parameter	parameter	ADJ
fcis-27234	51	16	optimization	optimization	NOUN
fcis-27234	51	17	problem	problem	NOUN
fcis-27234	51	18	in	in	ADP
fcis-27234	51	19	which	which	PRON
fcis-27234	51	20	several	several	ADJ
fcis-27234	51	21	parameters	parameter	NOUN
fcis-27234	51	22	need	need	VERB
fcis-27234	51	23	to	to	PART
fcis-27234	51	24	be	be	AUX
fcis-27234	51	25	determined	determine	VERB
fcis-27234	51	26	.	.	PUNCT
fcis-27234	52	1	to	to	PART
fcis-27234	52	2	solve	solve	VERB
fcis-27234	52	3	this	this	DET
fcis-27234	52	4	problem	problem	NOUN
fcis-27234	52	5	,	,	PUNCT
fcis-27234	52	6	we	we	PRON
fcis-27234	52	7	consider	consider	VERB
fcis-27234	52	8	using	use	VERB
fcis-27234	52	9	differential	differential	ADJ
fcis-27234	52	10	evolution	evolution	NOUN
fcis-27234	52	11	algorithm	algorithm	NOUN
fcis-27234	52	12	to	to	PART
fcis-27234	52	13	optimize	optimize	VERB
fcis-27234	52	14	the	the	DET
fcis-27234	52	15	solution	solution	NOUN
fcis-27234	52	16	.	.	PUNCT
fcis-27234	53	1	in	in	ADP
fcis-27234	53	2	practical	practical	ADJ
fcis-27234	53	3	applications	application	NOUN
fcis-27234	53	4	,	,	PUNCT
fcis-27234	53	5	we	we	PRON
fcis-27234	53	6	combine	combine	VERB
fcis-27234	53	7	the	the	DET
fcis-27234	53	8	differential	differential	ADJ
fcis-27234	53	9	evolution	evolution	NOUN
fcis-27234	53	10	algorithm	algorithm	NOUN
fcis-27234	53	11	with	with	ADP
fcis-27234	53	12	the	the	DET
fcis-27234	53	13	neural	neural	ADJ
fcis-27234	53	14	network	network	NOUN
fcis-27234	53	15	model	model	NOUN
fcis-27234	53	16	to	to	PART
fcis-27234	53	17	further	far	ADV
fcis-27234	53	18	improve	improve	VERB
fcis-27234	53	19	the	the	DET
fcis-27234	53	20	prediction	prediction	NOUN
fcis-27234	53	21	accuracy	accuracy	NOUN
fcis-27234	53	22	and	and	CCONJ
fcis-27234	53	23	stability	stability	NOUN
fcis-27234	53	24	.	.	PUNCT
fcis-27234	54	1	by	by	ADP
fcis-27234	54	2	adjusting	adjust	VERB
fcis-27234	54	3	the	the	DET
fcis-27234	54	4	parameters	parameter	NOUN
fcis-27234	54	5	of	of	ADP
fcis-27234	54	6	the	the	DET
fcis-27234	54	7	differential	differential	ADJ
fcis-27234	54	8	evolution	evolution	NOUN
fcis-27234	54	9	algorithm	algorithm	NOUN
fcis-27234	54	10	and	and	CCONJ
fcis-27234	54	11	setting	set	VERB
fcis-27234	54	12	the	the	DET
fcis-27234	54	13	fitness	fitness	NOUN
fcis-27234	54	14	function	function	NOUN
fcis-27234	54	15	,	,	PUNCT
fcis-27234	54	16	we	we	PRON
fcis-27234	54	17	can	can	AUX
fcis-27234	54	18	better	well	ADV
fcis-27234	54	19	control	control	VERB
fcis-27234	54	20	the	the	DET
fcis-27234	54	21	optimization	optimization	NOUN
fcis-27234	54	22	process	process	NOUN
fcis-27234	54	23	and	and	CCONJ
fcis-27234	54	24	obtain	obtain	VERB
fcis-27234	54	25	better	well	ADJ
fcis-27234	54	26	prediction	prediction	NOUN
fcis-27234	54	27	results	result	NOUN
fcis-27234	54	28	.	.	PUNCT
fcis-27234	55	1	therefore	therefore	ADV
fcis-27234	55	2	,	,	PUNCT
fcis-27234	55	3	we	we	PRON
fcis-27234	55	4	choose	choose	VERB
fcis-27234	55	5	differential	differential	ADJ
fcis-27234	55	6	evolution	evolution	NOUN
fcis-27234	55	7	algorithm	algorithm	NOUN
fcis-27234	55	8	,	,	PUNCT
fcis-27234	55	9	which	which	PRON
fcis-27234	55	10	can	can	AUX
fcis-27234	55	11	better	well	ADV
fcis-27234	55	12	solve	solve	VERB
fcis-27234	55	13	the	the	DET
fcis-27234	55	14	global	global	ADJ
fcis-27234	55	15	optimal	optimal	ADJ
fcis-27234	55	16	problem	problem	NOUN
fcis-27234	55	17	,	,	PUNCT
fcis-27234	55	18	to	to	PART
fcis-27234	55	19	optimize	optimize	VERB
fcis-27234	55	20	and	and	CCONJ
fcis-27234	55	21	solve	solve	VERB
fcis-27234	55	22	the	the	DET
fcis-27234	55	23	parameters	parameter	NOUN
fcis-27234	55	24	[	[	X
fcis-27234	55	25	7	7	NUM
fcis-27234	55	26	]	]	PUNCT
fcis-27234	55	27	.	.	PUNCT
fcis-27234	56	1	the	the	DET
fcis-27234	56	2	following	follow	VERB
fcis-27234	56	3	are	be	AUX
fcis-27234	56	4	the	the	DET
fcis-27234	56	5	solving	solve	VERB
fcis-27234	56	6	steps	step	NOUN
fcis-27234	56	7	:	:	PUNCT
fcis-27234	56	8	through	through	ADP
fcis-27234	56	9	differential	differential	ADJ
fcis-27234	56	10	evolution	evolution	NOUN
fcis-27234	56	11	algorithm	algorithm	NOUN
fcis-27234	56	12	optimization	optimization	NOUN
fcis-27234	56	13	,	,	PUNCT
fcis-27234	56	14	we	we	PRON
fcis-27234	56	15	find	find	VERB
fcis-27234	56	16	the	the	DET
fcis-27234	56	17	optimization	optimization	NOUN
fcis-27234	56	18	results	result	NOUN
fcis-27234	56	19	of	of	ADP
fcis-27234	56	20	seven	seven	NUM
fcis-27234	56	21	parameters	parameter	NOUN
fcis-27234	56	22	.	.	PUNCT
fcis-27234	57	1	the	the	DET
fcis-27234	57	2	results	result	NOUN
fcis-27234	57	3	are	be	AUX
fcis-27234	57	4	:	:	PUNCT
fcis-27234	57	5	1	1	NUM
fcis-27234	57	6	26.9884001822349	26.9884001822349	NUM
fcis-27234	57	7	2	2	NUM
fcis-27234	57	8	0.0444929670892593	0.0444929670892593	NUM
fcis-27234	57	9	3	3	NUM
fcis-27234	57	10	1.90076018991413	1.90076018991413	NUM
fcis-27234	57	11	4	4	NUM
fcis-27234	57	12	81.9846295969476	81.9846295969476	NUM
fcis-27234	57	13	x	x	SYM
fcis-27234	57	14	x	x	PUNCT
fcis-27234	57	15	x	x	PUNCT
fcis-27234	57	16	x	x	SYM
fcis-27234	57	17			NUM
fcis-27234	57	18			NUM
fcis-27234	57	19			PROPN
fcis-27234	57	20			NOUN
fcis-27234	57	21			NUM
fcis-27234	57	22	(	(	PUNCT
fcis-27234	57	23	4	4	NUM
fcis-27234	57	24	)	)	PUNCT
fcis-27234	57	25	through	through	ADP
fcis-27234	57	26	the	the	DET
fcis-27234	57	27	analysis	analysis	NOUN
fcis-27234	57	28	,	,	PUNCT
fcis-27234	57	29	we	we	PRON
fcis-27234	57	30	can	can	AUX
fcis-27234	57	31	draw	draw	VERB
fcis-27234	57	32	the	the	DET
fcis-27234	57	33	following	following	ADJ
fcis-27234	57	34	conclusions	conclusion	NOUN
fcis-27234	57	35	:	:	PUNCT
fcis-27234	57	36	1	1	X
fcis-27234	57	37	.	.	X
fcis-27234	57	38	differential	differential	ADJ
fcis-27234	57	39	evolution	evolution	NOUN
fcis-27234	57	40	algorithm	algorithm	NOUN
fcis-27234	57	41	can	can	AUX
fcis-27234	57	42	effectively	effectively	ADV
fcis-27234	57	43	deal	deal	VERB
fcis-27234	57	44	with	with	ADP
fcis-27234	57	45	regression	regression	NOUN
fcis-27234	57	46	problems	problem	NOUN
fcis-27234	57	47	and	and	CCONJ
fcis-27234	57	48	get	get	VERB
fcis-27234	57	49	better	well	ADJ
fcis-27234	57	50	fitting	fitting	ADJ
fcis-27234	57	51	results	result	NOUN
fcis-27234	57	52	.	.	PUNCT
fcis-27234	58	1	2	2	X
fcis-27234	58	2	.	.	X
fcis-27234	58	3	filtering	filter	VERB
fcis-27234	58	4	technology	technology	NOUN
fcis-27234	58	5	can	can	AUX
fcis-27234	58	6	effectively	effectively	ADV
fcis-27234	58	7	remove	remove	VERB
fcis-27234	58	8	the	the	DET
fcis-27234	58	9	noise	noise	NOUN
fcis-27234	58	10	in	in	ADP
fcis-27234	58	11	the	the	DET
fcis-27234	58	12	data	datum	NOUN
fcis-27234	58	13	and	and	CCONJ
fcis-27234	58	14	improve	improve	VERB
fcis-27234	58	15	the	the	DET
fcis-27234	58	16	accuracy	accuracy	NOUN
fcis-27234	58	17	and	and	CCONJ
fcis-27234	58	18	reliability	reliability	NOUN
fcis-27234	58	19	of	of	ADP
fcis-27234	58	20	data	datum	NOUN
fcis-27234	58	21	.	.	PUNCT
fcis-27234	59	1	3	3	X
fcis-27234	59	2	.	.	PUNCT
fcis-27234	59	3	by	by	ADP
fcis-27234	59	4	comparing	compare	VERB
fcis-27234	59	5	the	the	DET
fcis-27234	59	6	actual	actual	ADJ
fcis-27234	59	7	value	value	NOUN
fcis-27234	59	8	with	with	ADP
fcis-27234	59	9	the	the	DET
fcis-27234	59	10	filtered	filter	VERB
fcis-27234	59	11	value	value	NOUN
fcis-27234	59	12	,	,	PUNCT
fcis-27234	59	13	it	it	PRON
fcis-27234	59	14	can	can	AUX
fcis-27234	59	15	be	be	AUX
fcis-27234	59	16	seen	see	VERB
fcis-27234	59	17	that	that	SCONJ
fcis-27234	59	18	the	the	DET
fcis-27234	59	19	filtering	filter	VERB
fcis-27234	59	20	technology	technology	NOUN
fcis-27234	59	21	can	can	AUX
fcis-27234	59	22	smooth	smooth	VERB
fcis-27234	59	23	the	the	DET
fcis-27234	59	24	data	datum	NOUN
fcis-27234	59	25	52	52	NUM
fcis-27234	59	26	better	well	ADJ
fcis-27234	59	27	,	,	PUNCT
fcis-27234	59	28	so	so	SCONJ
fcis-27234	59	29	as	as	SCONJ
fcis-27234	59	30	to	to	PART
fcis-27234	59	31	better	well	ADV
fcis-27234	59	32	reflect	reflect	VERB
fcis-27234	59	33	the	the	DET
fcis-27234	59	34	overall	overall	ADJ
fcis-27234	59	35	trend	trend	NOUN
fcis-27234	59	36	and	and	CCONJ
fcis-27234	59	37	rule	rule	NOUN
fcis-27234	59	38	of	of	ADP
fcis-27234	59	39	the	the	DET
fcis-27234	59	40	data	datum	NOUN
fcis-27234	59	41	.	.	PUNCT
fcis-27234	60	1	2.5	2.5	NUM
fcis-27234	60	2	.	.	PUNCT
fcis-27234	61	1	bp	bp	PROPN
fcis-27234	61	2	neural	neural	PROPN
fcis-27234	61	3	network	network	PROPN
fcis-27234	61	4	prediction	prediction	NOUN
fcis-27234	61	5	model	model	NOUN
fcis-27234	61	6	bp	bp	PROPN
fcis-27234	61	7	neural	neural	PROPN
fcis-27234	61	8	network	network	NOUN
fcis-27234	61	9	is	be	AUX
fcis-27234	61	10	a	a	DET
fcis-27234	61	11	multi	multi	ADJ
fcis-27234	61	12	-	-	ADJ
fcis-27234	61	13	layer	layer	ADJ
fcis-27234	61	14	feedforward	feedforward	NOUN
fcis-27234	61	15	algorithm	algorithm	NOUN
fcis-27234	61	16	,	,	PUNCT
fcis-27234	61	17	which	which	PRON
fcis-27234	61	18	is	be	AUX
fcis-27234	61	19	composed	compose	VERB
fcis-27234	61	20	of	of	ADP
fcis-27234	61	21	input	input	NOUN
fcis-27234	61	22	layer	layer	NOUN
fcis-27234	61	23	,	,	PUNCT
fcis-27234	61	24	hidden	hide	VERB
fcis-27234	61	25	layer	layer	NOUN
fcis-27234	61	26	and	and	CCONJ
fcis-27234	61	27	output	output	NOUN
fcis-27234	61	28	layer	layer	NOUN
fcis-27234	61	29	.	.	PUNCT
fcis-27234	62	1	there	there	PRON
fcis-27234	62	2	are	be	VERB
fcis-27234	62	3	working	work	VERB
fcis-27234	62	4	signals	signal	NOUN
fcis-27234	62	5	and	and	CCONJ
fcis-27234	62	6	error	error	NOUN
fcis-27234	62	7	signals	signal	NOUN
fcis-27234	62	8	propagating	propagate	VERB
fcis-27234	62	9	between	between	ADP
fcis-27234	62	10	layers	layer	NOUN
fcis-27234	62	11	[	[	X
fcis-27234	62	12	8	8	NUM
fcis-27234	62	13	]	]	PUNCT
fcis-27234	62	14	.	.	PUNCT
fcis-27234	63	1	the	the	DET
fcis-27234	63	2	operation	operation	NOUN
fcis-27234	63	3	principle	principle	NOUN
fcis-27234	63	4	of	of	ADP
fcis-27234	63	5	bp	bp	PROPN
fcis-27234	63	6	neural	neural	ADJ
fcis-27234	63	7	network	network	NOUN
fcis-27234	63	8	is	be	AUX
fcis-27234	63	9	as	as	SCONJ
fcis-27234	63	10	follows	follow	VERB
fcis-27234	63	11	:	:	PUNCT
fcis-27234	63	12	remember	remember	VERB
fcis-27234	63	13	the	the	DET
fcis-27234	63	14	training	training	NOUN
fcis-27234	63	15	set	set	VERB
fcis-27234	63	16	as	as	ADP
fcis-27234	63	17	,	,	PUNCT
fcis-27234	63	18	,	,	PUNCT
fcis-27234	63	19	,	,	PUNCT
fcis-27234	63	20	,	,	PUNCT
fcis-27234	63	21	…	…	PUNCT
fcis-27234	63	22	,	,	PUNCT
fcis-27234	63	23	,	,	PUNCT
fcis-27234	63	24	,	,	PUNCT
fcis-27234	63	25	∈	∈	PROPN
fcis-27234	63	26	,	,	PUNCT
fcis-27234	63	27	∈	∈	PROPN
fcis-27234	63	28	and	and	CCONJ
fcis-27234	63	29	the	the	DET
fcis-27234	63	30	output	output	NOUN
fcis-27234	63	31	as	as	ADP
fcis-27234	63	32	,	,	PUNCT
fcis-27234	63	33	,	,	PUNCT
fcis-27234	63	34	…	…	PUNCT
fcis-27234	63	35	,	,	PUNCT
fcis-27234	63	36	.then	.then	ADP
fcis-27234	63	37	:	:	PUNCT
fcis-27234	63	38	∑	∑	INTJ
fcis-27234	63	39	  	  	SPACE
fcis-27234	63	40	.	.	PUNCT
fcis-27234	64	1	where	where	SCONJ
fcis-27234	64	2	,	,	PUNCT
fcis-27234	64	3	is	be	AUX
fcis-27234	64	4	the	the	DET
fcis-27234	64	5	connection	connection	NOUN
fcis-27234	64	6	weight	weight	NOUN
fcis-27234	64	7	of	of	ADP
fcis-27234	64	8	the	the	DET
fcis-27234	64	9	th	th	X
fcis-27234	64	10	neuron	neuron	NOUN
fcis-27234	64	11	to	to	ADP
fcis-27234	64	12	the	the	DET
fcis-27234	64	13	th	th	NUM
fcis-27234	64	14	output	output	NOUN
fcis-27234	64	15	.	.	PUNCT
fcis-27234	65	1	when	when	SCONJ
fcis-27234	65	2	the	the	DET
fcis-27234	65	3	network	network	NOUN
fcis-27234	65	4	is	be	AUX
fcis-27234	65	5	recorded	record	VERB
fcis-27234	65	6	on	on	ADP
fcis-27234	65	7	,	,	PUNCT
fcis-27234	65	8	,	,	PUNCT
fcis-27234	65	9	the	the	DET
fcis-27234	65	10	error	error	NOUN
fcis-27234	65	11	is	be	AUX
fcis-27234	65	12	:	:	PUNCT
fcis-27234	65	13			NOUN
fcis-27234	65	14	2	2	ADJ
fcis-27234	65	15	1	1	NUM
fcis-27234	65	16	ˆ	ˆ	NUM
fcis-27234	65	17	1	1	NUM
fcis-27234	65	18	2	2	NUM
fcis-27234	65	19	l	l	NOUN
fcis-27234	65	20	k	k	NOUN
fcis-27234	66	1	k	k	PROPN
fcis-27234	66	2	k	k	PROPN
fcis-27234	66	3	j	j	PROPN
fcis-27234	66	4	j	j	PROPN
fcis-27234	66	5	j	j	PROPN
fcis-27234	66	6	e	e	PROPN
fcis-27234	66	7	y	y	PROPN
fcis-27234	66	8	y	y	PROPN
fcis-27234	66	9			NUM
fcis-27234	66	10			NUM
fcis-27234	67	1			X
fcis-27234	67	2	(	(	PUNCT
fcis-27234	67	3	5	5	NUM
fcis-27234	67	4	)	)	PUNCT
fcis-27234	67	5	when	when	SCONJ
fcis-27234	67	6	the	the	DET
fcis-27234	67	7	neural	neural	ADJ
fcis-27234	67	8	network	network	NOUN
fcis-27234	67	9	completes	complete	VERB
fcis-27234	67	10	the	the	DET
fcis-27234	67	11	forward	forward	ADJ
fcis-27234	67	12	calculation	calculation	NOUN
fcis-27234	67	13	,	,	PUNCT
fcis-27234	67	14	the	the	DET
fcis-27234	67	15	predicted	predict	VERB
fcis-27234	67	16	value	value	NOUN
fcis-27234	67	17	is	be	AUX
fcis-27234	67	18	subtracted	subtract	VERB
fcis-27234	67	19	from	from	ADP
fcis-27234	67	20	the	the	DET
fcis-27234	67	21	actual	actual	ADJ
fcis-27234	67	22	value	value	NOUN
fcis-27234	67	23	to	to	PART
fcis-27234	67	24	get	get	VERB
fcis-27234	67	25	the	the	DET
fcis-27234	67	26	error	error	NOUN
fcis-27234	67	27	value	value	NOUN
fcis-27234	67	28	,	,	PUNCT
fcis-27234	67	29	and	and	CCONJ
fcis-27234	67	30	then	then	ADV
fcis-27234	67	31	the	the	DET
fcis-27234	67	32	weight	weight	NOUN
fcis-27234	67	33	threshold	threshold	NOUN
fcis-27234	67	34	of	of	ADP
fcis-27234	67	35	the	the	DET
fcis-27234	67	36	neural	neural	ADJ
fcis-27234	67	37	network	network	NOUN
fcis-27234	67	38	is	be	AUX
fcis-27234	67	39	adjusted	adjust	VERB
fcis-27234	67	40	by	by	ADP
fcis-27234	67	41	backpropagation	backpropagation	NOUN
fcis-27234	67	42	.	.	PUNCT
fcis-27234	68	1	the	the	DET
fcis-27234	68	2	iterative	iterative	NOUN
fcis-27234	68	3	updating	update	VERB
fcis-27234	68	4	formula	formula	NOUN
fcis-27234	68	5	of	of	ADP
fcis-27234	68	6	and	and	CCONJ
fcis-27234	68	7	is	be	AUX
fcis-27234	68	8	as	as	SCONJ
fcis-27234	68	9	follows	follow	VERB
fcis-27234	68	10	:	:	PUNCT
fcis-27234	68	11			PROPN
fcis-27234	68	12			NUM
fcis-27234	68	13	'	'	PUNCT
fcis-27234	68	14	ˆ	ˆ	ADJ
fcis-27234	68	15	ˆ	ˆ	NOUN
fcis-27234	68	16	ˆ	ˆ	NOUN
fcis-27234	68	17	ˆ	ˆ	ADV
fcis-27234	68	18	(	(	PUNCT
fcis-27234	68	19	)	)	PUNCT
fcis-27234	68	20	hj	hj	PROPN
fcis-27234	68	21	j	j	PROPN
fcis-27234	68	22	h	h	PROPN
fcis-27234	69	1	j	j	PROPN
fcis-27234	69	2	j	j	PROPN
fcis-27234	70	1	k	k	PROPN
fcis-27234	70	2	j	j	PROPN
fcis-27234	71	1	k	k	PROPN
fcis-27234	71	2	k	k	PROPN
fcis-27234	71	3	kk	kk	PROPN
fcis-27234	71	4	j	j	PROPN
fcis-27234	72	1	j	j	PROPN
fcis-27234	73	1	j	j	PROPN
fcis-27234	73	2	jk	jk	PROPN
fcis-27234	73	3	j	j	PROPN
fcis-27234	74	1	j	j	PROPN
fcis-27234	74	2	g	g	PROPN
fcis-27234	74	3	b	b	PROPN
fcis-27234	74	4	g	g	PROPN
fcis-27234	74	5	ye	ye	PRON
fcis-27234	74	6	g	g	PROPN
fcis-27234	74	7	y	y	PROPN
fcis-27234	74	8	y	y	PROPN
fcis-27234	74	9	y	y	PROPN
fcis-27234	74	10	y	y	PROPN
fcis-27234	74	11			X
fcis-27234	74	12			PUNCT
fcis-27234	74	13			SYM
fcis-27234	74	14			NUM
fcis-27234	74	15			PROPN
fcis-27234	74	16			NOUN
fcis-27234	74	17			NUM
fcis-27234	74	18			X
fcis-27234	75	1			NUM
fcis-27234	75	2			NOUN
fcis-27234	75	3			VERB
fcis-27234	75	4			PROPN
fcis-27234	75	5			PROPN
fcis-27234	75	6			PRON
fcis-27234	76	1			NUM
fcis-27234	76	2			PROPN
fcis-27234	76	3			PROPN
fcis-27234	76	4			NOUN
fcis-27234	76	5			ADJ
fcis-27234	76	6			PROPN
fcis-27234	76	7	(	(	PUNCT
fcis-27234	76	8	6	6	NUM
fcis-27234	76	9	)	)	PUNCT
fcis-27234	76	10	where	where	SCONJ
fcis-27234	76	11	,	,	PUNCT
fcis-27234	76	12	is	be	AUX
fcis-27234	76	13	the	the	DET
fcis-27234	76	14	input	input	NOUN
fcis-27234	76	15	data	datum	NOUN
fcis-27234	76	16	of	of	ADP
fcis-27234	76	17	the	the	DET
fcis-27234	76	18	neuron	neuron	NOUN
fcis-27234	76	19	.	.	PUNCT
fcis-27234	77	1	based	base	VERB
fcis-27234	77	2	on	on	ADP
fcis-27234	77	3	this	this	PRON
fcis-27234	77	4	,	,	PUNCT
fcis-27234	77	5	the	the	DET
fcis-27234	77	6	neural	neural	ADJ
fcis-27234	77	7	network	network	NOUN
fcis-27234	77	8	constantly	constantly	ADV
fcis-27234	77	9	adjusts	adjust	VERB
fcis-27234	77	10	the	the	DET
fcis-27234	77	11	weights	weight	NOUN
fcis-27234	77	12	and	and	CCONJ
fcis-27234	77	13	thresholds	threshold	NOUN
fcis-27234	77	14	in	in	ADP
fcis-27234	77	15	its	its	PRON
fcis-27234	77	16	training	training	NOUN
fcis-27234	77	17	process	process	NOUN
fcis-27234	77	18	,	,	PUNCT
fcis-27234	77	19	so	so	SCONJ
fcis-27234	77	20	that	that	SCONJ
fcis-27234	77	21	the	the	DET
fcis-27234	77	22	prediction	prediction	NOUN
fcis-27234	77	23	error	error	NOUN
fcis-27234	77	24	of	of	ADP
fcis-27234	77	25	the	the	DET
fcis-27234	77	26	neural	neural	ADJ
fcis-27234	77	27	network	network	NOUN
fcis-27234	77	28	is	be	AUX
fcis-27234	77	29	constantly	constantly	ADV
fcis-27234	77	30	approaching	approach	VERB
fcis-27234	77	31	0	0	NUM
fcis-27234	77	32	.	.	PUNCT
fcis-27234	77	33	2.6	2.6	NUM
fcis-27234	77	34	.	.	PUNCT
fcis-27234	77	35	hybrid	hybrid	ADJ
fcis-27234	77	36	model	model	NOUN
fcis-27234	77	37	solution	solution	NOUN
fcis-27234	77	38	results	result	VERB
fcis-27234	77	39	for	for	ADP
fcis-27234	77	40	each	each	DET
fcis-27234	77	41	period	period	NOUN
fcis-27234	77	42	,	,	PUNCT
fcis-27234	77	43	the	the	DET
fcis-27234	77	44	observed	observe	VERB
fcis-27234	77	45	value	value	NOUN
fcis-27234	77	46	is	be	AUX
fcis-27234	77	47	the	the	DET
fcis-27234	77	48	data	datum	NOUN
fcis-27234	77	49	that	that	PRON
fcis-27234	77	50	actually	actually	ADV
fcis-27234	77	51	occurred	occur	VERB
fcis-27234	77	52	,	,	PUNCT
fcis-27234	77	53	the	the	DET
fcis-27234	77	54	model	model	NOUN
fcis-27234	77	55	predicted	predict	VERB
fcis-27234	77	56	value	value	NOUN
fcis-27234	77	57	is	be	AUX
fcis-27234	77	58	the	the	DET
fcis-27234	77	59	prediction	prediction	NOUN
fcis-27234	77	60	result	result	NOUN
fcis-27234	77	61	based	base	VERB
fcis-27234	77	62	on	on	ADP
fcis-27234	77	63	the	the	DET
fcis-27234	77	64	bp	bp	PROPN
fcis-27234	77	65	neural	neural	PROPN
fcis-27234	77	66	network	network	NOUN
fcis-27234	77	67	prediction	prediction	NOUN
fcis-27234	77	68	model	model	NOUN
fcis-27234	77	69	,	,	PUNCT
fcis-27234	77	70	and	and	CCONJ
fcis-27234	77	71	the	the	DET
fcis-27234	77	72	fit	fit	ADJ
fcis-27234	77	73	value	value	NOUN
fcis-27234	77	74	of	of	ADP
fcis-27234	77	75	the	the	DET
fcis-27234	77	76	autoregressive	autoregressive	ADJ
fcis-27234	77	77	moving	move	VERB
fcis-27234	77	78	average	average	ADJ
fcis-27234	77	79	model	model	NOUN
fcis-27234	77	80	is	be	AUX
fcis-27234	77	81	the	the	DET
fcis-27234	77	82	fit	fit	ADJ
fcis-27234	77	83	result	result	NOUN
fcis-27234	77	84	based	base	VERB
fcis-27234	77	85	on	on	ADP
fcis-27234	77	86	the	the	DET
fcis-27234	77	87	differential	differential	ADJ
fcis-27234	77	88	evolution	evolution	NOUN
fcis-27234	77	89	algorithm	algorithm	NOUN
fcis-27234	77	90	.	.	PUNCT
fcis-27234	78	1	by	by	ADP
fcis-27234	78	2	comparing	compare	VERB
fcis-27234	78	3	the	the	DET
fcis-27234	78	4	values	value	NOUN
fcis-27234	78	5	of	of	ADP
fcis-27234	78	6	these	these	DET
fcis-27234	78	7	three	three	NUM
fcis-27234	78	8	labels	label	NOUN
fcis-27234	78	9	,	,	PUNCT
fcis-27234	78	10	we	we	PRON
fcis-27234	78	11	can	can	AUX
fcis-27234	78	12	assess	assess	VERB
fcis-27234	78	13	the	the	DET
fcis-27234	78	14	model	model	NOUN
fcis-27234	78	15	's	's	PART
fcis-27234	78	16	predictive	predictive	ADJ
fcis-27234	78	17	power	power	NOUN
fcis-27234	78	18	and	and	CCONJ
fcis-27234	78	19	fitting	fitting	ADJ
fcis-27234	78	20	effect	effect	NOUN
fcis-27234	78	21	.	.	PUNCT
fcis-27234	79	1	from	from	ADP
fcis-27234	79	2	the	the	DET
fcis-27234	79	3	analysis	analysis	NOUN
fcis-27234	79	4	,	,	PUNCT
fcis-27234	79	5	we	we	PRON
fcis-27234	79	6	can	can	AUX
fcis-27234	79	7	see	see	VERB
fcis-27234	79	8	that	that	SCONJ
fcis-27234	79	9	the	the	DET
fcis-27234	79	10	maximum	maximum	ADJ
fcis-27234	79	11	value	value	NOUN
fcis-27234	79	12	occurs	occur	VERB
fcis-27234	79	13	in	in	ADP
fcis-27234	79	14	january	january	PROPN
fcis-27234	79	15	2036	2036	NUM
fcis-27234	79	16	,	,	PUNCT
fcis-27234	79	17	and	and	CCONJ
fcis-27234	79	18	the	the	DET
fcis-27234	79	19	corresponding	corresponding	ADJ
fcis-27234	79	20	sunspot	sunspot	NOUN
fcis-27234	79	21	quantity	quantity	NOUN
fcis-27234	79	22	is	be	AUX
fcis-27234	79	23	154.3	154.3	NUM
fcis-27234	79	24	.	.	PUNCT
fcis-27234	80	1	therefore	therefore	ADV
fcis-27234	80	2	,	,	PUNCT
fcis-27234	80	3	back	back	ADV
fcis-27234	80	4	can	can	AUX
fcis-27234	80	5	be	be	AUX
fcis-27234	80	6	performed	perform	VERB
fcis-27234	80	7	,	,	PUNCT
fcis-27234	80	8	and	and	CCONJ
fcis-27234	80	9	the	the	DET
fcis-27234	80	10	result	result	NOUN
fcis-27234	80	11	is	be	AUX
fcis-27234	80	12	shown	show	VERB
fcis-27234	80	13	in	in	ADP
fcis-27234	80	14	table	table	NOUN
fcis-27234	80	15	1	1	NUM
fcis-27234	80	16	:	:	PUNCT
fcis-27234	80	17	table	table	NOUN
fcis-27234	80	18	1	1	NUM
fcis-27234	80	19	.	.	PUNCT
fcis-27234	80	20	prediction	prediction	NOUN
fcis-27234	80	21	result	result	VERB
fcis-27234	80	22	solar	solar	ADJ
fcis-27234	80	23	cycle	cycle	NOUN
fcis-27234	80	24	start	start	VERB
fcis-27234	80	25	time	time	NOUN
fcis-27234	80	26	end	end	NOUN
fcis-27234	80	27	time	time	NOUN
fcis-27234	80	28	maximum	maximum	PROPN
fcis-27234	80	29	duration	duration	NOUN
fcis-27234	80	30	solar	solar	ADJ
fcis-27234	80	31	cycle	cycle	NOUN
fcis-27234	80	32	duration	duration	NOUN
fcis-27234	80	33	23	23	NUM
fcis-27234	80	34	1996	1996	NUM
fcis-27234	80	35	-	-	SYM
fcis-27234	80	36	08	08	NUM
fcis-27234	80	37	2001	2001	NUM
fcis-27234	80	38	-	-	SYM
fcis-27234	80	39	11	11	NUM
fcis-27234	80	40	5.25	5.25	NUM
fcis-27234	80	41	12.6	12.6	NUM
fcis-27234	80	42	24	24	NUM
fcis-27234	80	43	2008	2008	NUM
fcis-27234	80	44	-	-	SYM
fcis-27234	80	45	12	12	NUM
fcis-27234	80	46	2014	2014	NUM
fcis-27234	80	47	-	-	SYM
fcis-27234	80	48	04	04	NUM
fcis-27234	81	1	5.333333333	5.333333333	NUM
fcis-27234	81	2	11	11	NUM
fcis-27234	81	3	25	25	NUM
fcis-27234	81	4	2019	2019	NUM
fcis-27234	81	5	-	-	SYM
fcis-27234	81	6	12	12	NUM
fcis-27234	81	7	2025	2025	NUM
fcis-27234	81	8	-	-	SYM
fcis-27234	81	9	5	5	NUM
fcis-27234	81	10	5.5171	5.5171	NUM
fcis-27234	81	11	10.839	10.839	NUM
fcis-27234	81	12	26	26	NUM
fcis-27234	81	13	2031	2031	NUM
fcis-27234	81	14	-	-	SYM
fcis-27234	81	15	9	9	NUM
fcis-27234	81	16	2037	2037	NUM
fcis-27234	81	17	-	-	SYM
fcis-27234	81	18	3	3	NUM
fcis-27234	81	19	5.368	5.368	NUM
fcis-27234	81	20	10.8	10.8	NUM
fcis-27234	81	21	in	in	ADP
fcis-27234	81	22	order	order	NOUN
fcis-27234	81	23	to	to	PART
fcis-27234	81	24	predict	predict	VERB
fcis-27234	81	25	the	the	DET
fcis-27234	81	26	start	start	ADJ
fcis-27234	81	27	time	time	NOUN
fcis-27234	81	28	and	and	CCONJ
fcis-27234	81	29	duration	duration	NOUN
fcis-27234	81	30	of	of	ADP
fcis-27234	81	31	the	the	DET
fcis-27234	81	32	solar	solar	ADJ
fcis-27234	81	33	maximum	maximum	NOUN
fcis-27234	81	34	in	in	ADP
fcis-27234	81	35	the	the	DET
fcis-27234	81	36	next	next	ADJ
fcis-27234	81	37	solar	solar	ADJ
fcis-27234	81	38	activity	activity	NOUN
fcis-27234	81	39	cycle	cycle	NOUN
fcis-27234	81	40	,	,	PUNCT
fcis-27234	81	41	pearson	pearson	PROPN
fcis-27234	81	42	's	's	PART
fcis-27234	81	43	correlation	correlation	NOUN
fcis-27234	81	44	coefficient	coefficient	NOUN
fcis-27234	81	45	detects	detect	NOUN
fcis-27234	81	46	that	that	SCONJ
fcis-27234	81	47	there	there	PRON
fcis-27234	81	48	is	be	VERB
fcis-27234	81	49	a	a	DET
fcis-27234	81	50	correlation	correlation	NOUN
fcis-27234	81	51	between	between	ADP
fcis-27234	81	52	the	the	DET
fcis-27234	81	53	maximum	maximum	NOUN
fcis-27234	81	54	and	and	CCONJ
fcis-27234	81	55	the	the	DET
fcis-27234	81	56	solar	solar	ADJ
fcis-27234	81	57	blackness	blackness	NOUN
fcis-27234	81	58	number	number	NOUN
fcis-27234	81	59	.	.	PUNCT
fcis-27234	82	1	therefore	therefore	ADV
fcis-27234	82	2	,	,	PUNCT
fcis-27234	82	3	a	a	DET
fcis-27234	82	4	single	single	ADJ
fcis-27234	82	5	linear	linear	ADJ
fcis-27234	82	6	regression	regression	NOUN
fcis-27234	82	7	is	be	AUX
fcis-27234	82	8	established	establish	VERB
fcis-27234	82	9	to	to	PART
fcis-27234	82	10	obtain	obtain	VERB
fcis-27234	82	11	the	the	DET
fcis-27234	82	12	negative	negative	ADJ
fcis-27234	82	13	correlation	correlation	NOUN
fcis-27234	82	14	between	between	ADP
fcis-27234	82	15	them	they	PRON
fcis-27234	82	16	before	before	ADV
fcis-27234	82	17	.	.	PUNCT
fcis-27234	83	1	based	base	VERB
fcis-27234	83	2	on	on	ADP
fcis-27234	83	3	adaptive	adaptive	ADJ
fcis-27234	83	4	multivariate	multivariate	NOUN
fcis-27234	83	5	nonlinear	nonlinear	ADJ
fcis-27234	83	6	regression	regression	NOUN
fcis-27234	83	7	-bp	-bp	X
fcis-27234	83	8	neural	neural	ADJ
fcis-27234	83	9	network	network	NOUN
fcis-27234	83	10	model	model	NOUN
fcis-27234	83	11	for	for	ADP
fcis-27234	83	12	solar	solar	ADJ
fcis-27234	83	13	activity	activity	NOUN
fcis-27234	83	14	prediction	prediction	NOUN
fcis-27234	83	15	,	,	PUNCT
fcis-27234	83	16	first	first	ADV
fcis-27234	83	17	establish	establish	VERB
fcis-27234	83	18	the	the	DET
fcis-27234	83	19	relationship	relationship	NOUN
fcis-27234	83	20	between	between	ADP
fcis-27234	83	21	time	time	NOUN
fcis-27234	83	22	and	and	CCONJ
fcis-27234	83	23	the	the	DET
fcis-27234	83	24	number	number	NOUN
fcis-27234	83	25	of	of	ADP
fcis-27234	83	26	sunspots	sunspot	NOUN
fcis-27234	83	27	model	model	NOUN
fcis-27234	83	28	,	,	PUNCT
fcis-27234	83	29	and	and	CCONJ
fcis-27234	83	30	then	then	ADV
fcis-27234	83	31	based	base	VERB
fcis-27234	83	32	on	on	ADP
fcis-27234	83	33	the	the	DET
fcis-27234	83	34	differential	differential	ADJ
fcis-27234	83	35	evolution	evolution	NOUN
fcis-27234	83	36	algorithm	algorithm	NOUN
fcis-27234	83	37	model	model	NOUN
fcis-27234	83	38	for	for	ADP
fcis-27234	83	39	solving	solving	NOUN
fcis-27234	83	40	,	,	PUNCT
fcis-27234	83	41	and	and	CCONJ
fcis-27234	83	42	then	then	ADV
fcis-27234	83	43	use	use	VERB
fcis-27234	83	44	the	the	DET
fcis-27234	83	45	differential	differential	ADJ
fcis-27234	83	46	evolution	evolution	NOUN
fcis-27234	83	47	algorithm	algorithm	NOUN
fcis-27234	83	48	model	model	NOUN
fcis-27234	83	49	and	and	CCONJ
fcis-27234	83	50	bp	bp	PROPN
fcis-27234	83	51	neural	neural	ADJ
fcis-27234	83	52	network	network	NOUN
fcis-27234	83	53	model	model	NOUN
fcis-27234	83	54	for	for	ADP
fcis-27234	83	55	mixed	mixed	ADJ
fcis-27234	83	56	model	model	NOUN
fcis-27234	83	57	to	to	PART
fcis-27234	83	58	solve	solve	VERB
fcis-27234	83	59	,	,	PUNCT
fcis-27234	83	60	calculate	calculate	VERB
fcis-27234	83	61	the	the	DET
fcis-27234	83	62	maximum	maximum	NOUN
fcis-27234	83	63	for	for	ADP
fcis-27234	83	64	back	back	ADJ
fcis-27234	83	65	generation	generation	NOUN
fcis-27234	83	66	,	,	PUNCT
fcis-27234	83	67	to	to	PART
fcis-27234	83	68	achieve	achieve	VERB
fcis-27234	83	69	the	the	DET
fcis-27234	83	70	next	next	ADJ
fcis-27234	83	71	cycle	cycle	NOUN
fcis-27234	83	72	prediction	prediction	NOUN
fcis-27234	83	73	.	.	PUNCT
fcis-27234	84	1	3	3	X
fcis-27234	84	2	.	.	X
fcis-27234	84	3	prediction	prediction	NOUN
fcis-27234	84	4	of	of	ADP
fcis-27234	84	5	the	the	DET
fcis-27234	84	6	number	number	NOUN
fcis-27234	84	7	and	and	CCONJ
fcis-27234	84	8	area	area	NOUN
fcis-27234	84	9	of	of	ADP
fcis-27234	84	10	sunspots	sunspot	NOUN
fcis-27234	84	11	to	to	PART
fcis-27234	84	12	predict	predict	VERB
fcis-27234	84	13	the	the	DET
fcis-27234	84	14	number	number	NOUN
fcis-27234	84	15	and	and	CCONJ
fcis-27234	84	16	area	area	NOUN
fcis-27234	84	17	of	of	ADP
fcis-27234	84	18	sunspots	sunspot	NOUN
fcis-27234	84	19	in	in	ADP
fcis-27234	84	20	the	the	DET
fcis-27234	84	21	current	current	ADJ
fcis-27234	84	22	and	and	CCONJ
fcis-27234	84	23	next	next	ADJ
fcis-27234	84	24	solar	solar	ADJ
fcis-27234	84	25	cycles	cycle	NOUN
fcis-27234	84	26	.	.	PUNCT
fcis-27234	85	1	we	we	PRON
fcis-27234	85	2	introduce	introduce	VERB
fcis-27234	85	3	fly	fly	VERB
fcis-27234	85	4	to	to	PART
fcis-27234	85	5	analyze	analyze	VERB
fcis-27234	85	6	periodicity	periodicity	NOUN
fcis-27234	85	7	,	,	PUNCT
fcis-27234	85	8	and	and	CCONJ
fcis-27234	85	9	combine	combine	VERB
fcis-27234	85	10	the	the	DET
fcis-27234	85	11	time	time	NOUN
fcis-27234	85	12	series	series	PROPN
fcis-27234	85	13	prediction	prediction	NOUN
fcis-27234	85	14	model	model	NOUN
fcis-27234	85	15	based	base	VERB
fcis-27234	85	16	on	on	ADP
fcis-27234	85	17	bp	bp	PROPN
fcis-27234	85	18	neural	neural	ADJ
fcis-27234	85	19	network	network	NOUN
fcis-27234	85	20	of	of	ADP
fcis-27234	85	21	particle	particle	NOUN
fcis-27234	85	22	swarm	swarm	NOUN
fcis-27234	85	23	optimization	optimization	NOUN
fcis-27234	85	24	to	to	PART
fcis-27234	85	25	build	build	VERB
fcis-27234	85	26	a	a	DET
fcis-27234	85	27	time	time	NOUN
fcis-27234	85	28	prediction	prediction	NOUN
fcis-27234	85	29	trend	trend	NOUN
fcis-27234	85	30	graph	graph	NOUN
fcis-27234	85	31	.	.	PUNCT
fcis-27234	86	1	3.1	3.1	NUM
fcis-27234	86	2	.	.	PUNCT
fcis-27234	87	1	fft	fft	PROPN
fcis-27234	87	2	analyzes	analyze	VERB
fcis-27234	87	3	periodic	periodic	ADJ
fcis-27234	87	4	data	datum	NOUN
fcis-27234	87	5	sunspot	sunspot	NOUN
fcis-27234	87	6	forecasts	forecast	NOUN
fcis-27234	87	7	are	be	AUX
fcis-27234	87	8	usually	usually	ADV
fcis-27234	87	9	derived	derive	VERB
fcis-27234	87	10	from	from	ADP
fcis-27234	87	11	monthly	monthly	ADJ
fcis-27234	87	12	averages	average	NOUN
fcis-27234	87	13	.	.	PUNCT
fcis-27234	88	1	for	for	ADP
fcis-27234	88	2	this	this	PRON
fcis-27234	88	3	,	,	PUNCT
fcis-27234	88	4	monthly	monthly	ADJ
fcis-27234	88	5	smoothing	smoothing	NOUN
fcis-27234	88	6	of	of	ADP
fcis-27234	88	7	sunspot	sunspot	NOUN
fcis-27234	88	8	numbers	number	NOUN
fcis-27234	88	9	is	be	AUX
fcis-27234	88	10	performed	perform	VERB
fcis-27234	88	11	to	to	PART
fcis-27234	88	12	plot	plot	VERB
fcis-27234	88	13	the	the	DET
fcis-27234	88	14	monthly	monthly	ADJ
fcis-27234	88	15	average	average	NOUN
fcis-27234	88	16	of	of	ADP
fcis-27234	88	17	sunspots	sunspot	NOUN
fcis-27234	88	18	over	over	ADP
fcis-27234	88	19	the	the	DET
fcis-27234	88	20	period	period	NOUN
fcis-27234	88	21	approximately	approximately	ADV
fcis-27234	88	22	1700	1700	NUM
fcis-27234	88	23	-	-	SYM
fcis-27234	88	24	2000	2000	NUM
fcis-27234	88	25	.	.	PUNCT
fcis-27234	89	1	to	to	PART
fcis-27234	89	2	get	get	VERB
fcis-27234	89	3	a	a	DET
fcis-27234	89	4	more	more	ADV
fcis-27234	89	5	detailed	detailed	ADJ
fcis-27234	89	6	view	view	NOUN
fcis-27234	89	7	of	of	ADP
fcis-27234	89	8	the	the	DET
fcis-27234	89	9	periodic	periodic	ADJ
fcis-27234	89	10	nature	nature	NOUN
fcis-27234	89	11	of	of	ADP
fcis-27234	89	12	sunspot	sunspot	NOUN
fcis-27234	89	13	activity	activity	NOUN
fcis-27234	89	14	,	,	PUNCT
fcis-27234	89	15	50	50	NUM
fcis-27234	89	16	years	year	NOUN
fcis-27234	89	17	of	of	ADP
fcis-27234	89	18	data	datum	NOUN
fcis-27234	89	19	will	will	AUX
fcis-27234	89	20	be	be	AUX
fcis-27234	89	21	plotted	plot	VERB
fcis-27234	89	22	,	,	PUNCT
fcis-27234	89	23	as	as	SCONJ
fcis-27234	89	24	shown	show	VERB
fcis-27234	89	25	in	in	ADP
fcis-27234	89	26	figure	figure	NOUN
fcis-27234	89	27	3	3	NUM
fcis-27234	89	28	.	.	PUNCT
fcis-27234	89	29	figure	figure	NOUN
fcis-27234	89	30	3	3	NUM
fcis-27234	89	31	.	.	PUNCT
fcis-27234	89	32	monthly	monthly	ADJ
fcis-27234	89	33	average	average	ADJ
fcis-27234	89	34	value	value	NOUN
fcis-27234	89	35	of	of	ADP
fcis-27234	89	36	sunspot	sunspot	NOUN
fcis-27234	89	37	number	number	NOUN
fcis-27234	89	38	therefore	therefore	ADV
fcis-27234	89	39	,	,	PUNCT
fcis-27234	89	40	the	the	DET
fcis-27234	89	41	number	number	NOUN
fcis-27234	89	42	and	and	CCONJ
fcis-27234	89	43	area	area	NOUN
fcis-27234	89	44	of	of	ADP
fcis-27234	89	45	sunspots	sunspot	NOUN
fcis-27234	89	46	can	can	AUX
fcis-27234	89	47	be	be	AUX
fcis-27234	89	48	analyzed	analyze	VERB
fcis-27234	89	49	periodically	periodically	ADV
fcis-27234	89	50	.	.	PUNCT
fcis-27234	90	1	3.2	3.2	NUM
fcis-27234	90	2	.	.	PUNCT
fcis-27234	90	3	establishment	establishment	NOUN
fcis-27234	90	4	of	of	ADP
fcis-27234	90	5	pso	pso	NOUN
fcis-27234	90	6	-	-	PUNCT
fcis-27234	90	7	bp	bp	PROPN
fcis-27234	90	8	prediction	prediction	NOUN
fcis-27234	90	9	model	model	NOUN
fcis-27234	90	10	during	during	ADP
fcis-27234	90	11	the	the	DET
fcis-27234	90	12	establishment	establishment	NOUN
fcis-27234	90	13	of	of	ADP
fcis-27234	90	14	bp	bp	PROPN
fcis-27234	90	15	neural	neural	ADJ
fcis-27234	90	16	network	network	NOUN
fcis-27234	90	17	,	,	PUNCT
fcis-27234	90	18	the	the	DET
fcis-27234	90	19	random	random	ADJ
fcis-27234	90	20	setting	setting	NOUN
fcis-27234	90	21	of	of	ADP
fcis-27234	90	22	connection	connection	NOUN
fcis-27234	90	23	weights	weight	NOUN
fcis-27234	90	24	will	will	AUX
fcis-27234	90	25	lead	lead	VERB
fcis-27234	90	26	to	to	ADP
fcis-27234	90	27	errors	error	NOUN
fcis-27234	90	28	in	in	ADP
fcis-27234	90	29	the	the	DET
fcis-27234	90	30	prediction	prediction	NOUN
fcis-27234	90	31	results	result	NOUN
fcis-27234	90	32	,	,	PUNCT
fcis-27234	90	33	and	and	CCONJ
fcis-27234	90	34	gradient	gradient	ADJ
fcis-27234	90	35	descent	descent	NOUN
fcis-27234	90	36	training	training	NOUN
fcis-27234	90	37	has	have	VERB
fcis-27234	90	38	the	the	DET
fcis-27234	90	39	shortcomings	shortcoming	NOUN
fcis-27234	90	40	of	of	ADP
fcis-27234	90	41	slow	slow	ADJ
fcis-27234	90	42	speed	speed	NOUN
fcis-27234	90	43	and	and	CCONJ
fcis-27234	90	44	local	local	ADJ
fcis-27234	90	45	minimum	minimum	NOUN
fcis-27234	90	46	,	,	PUNCT
fcis-27234	90	47	so	so	SCONJ
fcis-27234	90	48	it	it	PRON
fcis-27234	90	49	is	be	AUX
fcis-27234	90	50	difficult	difficult	ADJ
fcis-27234	90	51	to	to	PART
fcis-27234	90	52	achieve	achieve	VERB
fcis-27234	90	53	the	the	DET
fcis-27234	90	54	global	global	ADJ
fcis-27234	90	55	optimal	optimal	ADJ
fcis-27234	90	56	training	training	NOUN
fcis-27234	90	57	of	of	ADP
fcis-27234	90	58	neural	neural	ADJ
fcis-27234	90	59	network	network	NOUN
fcis-27234	90	60	.	.	PUNCT
fcis-27234	91	1	particle	particle	NOUN
fcis-27234	91	2	swarm	swarm	NOUN
fcis-27234	91	3	optimization	optimization	NOUN
fcis-27234	91	4	algorithm	algorithm	NOUN
fcis-27234	91	5	(	(	PUNCT
fcis-27234	91	6	pso	pso	NOUN
fcis-27234	91	7	)	)	PUNCT
fcis-27234	91	8	is	be	AUX
fcis-27234	91	9	used	use	VERB
fcis-27234	91	10	to	to	PART
fcis-27234	91	11	optimize	optimize	VERB
fcis-27234	91	12	bp	bp	PROPN
fcis-27234	91	13	neural	neural	ADJ
fcis-27234	91	14	network	network	NOUN
fcis-27234	91	15	,	,	PUNCT
fcis-27234	91	16	and	and	CCONJ
fcis-27234	91	17	the	the	DET
fcis-27234	91	18	prediction	prediction	NOUN
fcis-27234	91	19	accuracy	accuracy	NOUN
fcis-27234	91	20	and	and	CCONJ
fcis-27234	91	21	generalization	generalization	NOUN
fcis-27234	91	22	ability	ability	NOUN
fcis-27234	91	23	are	be	AUX
fcis-27234	91	24	improved	improve	VERB
fcis-27234	91	25	.	.	PUNCT
fcis-27234	92	1	the	the	DET
fcis-27234	92	2	construction	construction	NOUN
fcis-27234	92	3	process	process	NOUN
fcis-27234	92	4	is	be	AUX
fcis-27234	92	5	as	as	SCONJ
fcis-27234	92	6	follows	follow	VERB
fcis-27234	92	7	:	:	PUNCT
fcis-27234	92	8	(	(	PUNCT
fcis-27234	92	9	1	1	X
fcis-27234	92	10	)	)	PUNCT
fcis-27234	92	11	data	datum	NOUN
fcis-27234	92	12	normalization	normalization	NOUN
fcis-27234	92	13	,	,	PUNCT
fcis-27234	92	14	establish	establish	VERB
fcis-27234	92	15	bp	bp	PROPN
fcis-27234	92	16	neural	neural	ADJ
fcis-27234	92	17	network	network	NOUN
fcis-27234	92	18	,	,	PUNCT
fcis-27234	92	19	determine	determine	VERB
fcis-27234	92	20	the	the	DET
fcis-27234	92	21	topology	topology	NOUN
fcis-27234	92	22	and	and	CCONJ
fcis-27234	92	23	initialize	initialize	VERB
fcis-27234	92	24	the	the	DET
fcis-27234	92	25	weight	weight	NOUN
fcis-27234	92	26	and	and	CCONJ
fcis-27234	92	27	threshold	threshold	NOUN
fcis-27234	92	28	of	of	ADP
fcis-27234	92	29	the	the	DET
fcis-27234	92	30	network	network	NOUN
fcis-27234	92	31	;	;	PUNCT
fcis-27234	92	32	(	(	PUNCT
fcis-27234	92	33	2	2	X
fcis-27234	92	34	)	)	PUNCT
fcis-27234	92	35	initialize	initialize	VERB
fcis-27234	92	36	pso	pso	NOUN
fcis-27234	92	37	parameters	parameter	NOUN
fcis-27234	92	38	,	,	PUNCT
fcis-27234	92	39	such	such	ADJ
fcis-27234	92	40	as	as	ADP
fcis-27234	92	41	the	the	DET
fcis-27234	92	42	maximum	maximum	ADJ
fcis-27234	92	43	number	number	NOUN
fcis-27234	92	44	of	of	ADP
fcis-27234	92	45	iterations	iteration	NOUN
fcis-27234	92	46	,	,	PUNCT
fcis-27234	92	47	population	population	NOUN
fcis-27234	92	48	size	size	NOUN
fcis-27234	92	49	,	,	PUNCT
fcis-27234	92	50	individual	individual	ADJ
fcis-27234	92	51	learning	learning	NOUN
fcis-27234	92	52	factor	factor	NOUN
fcis-27234	92	53	,	,	PUNCT
fcis-27234	92	54	social	social	ADJ
fcis-27234	92	55	learning	learning	NOUN
fcis-27234	92	56	factor	factor	NOUN
fcis-27234	92	57	,	,	PUNCT
fcis-27234	92	58	inertia	inertia	NOUN
fcis-27234	92	59	weight	weight	NOUN
fcis-27234	92	60	,	,	PUNCT
fcis-27234	92	61	etc	etc	X
fcis-27234	92	62	.	.	X
fcis-27234	92	63	;	;	PUNCT
fcis-27234	92	64	53	53	NUM
fcis-27234	92	65	(	(	PUNCT
fcis-27234	92	66	3	3	NUM
fcis-27234	92	67	)	)	PUNCT
fcis-27234	92	68	initialize	initialize	VERB
fcis-27234	92	69	the	the	DET
fcis-27234	92	70	population	population	NOUN
fcis-27234	92	71	position	position	NOUN
fcis-27234	92	72	of	of	ADP
fcis-27234	92	73	pso	pso	NOUN
fcis-27234	92	74	,	,	PUNCT
fcis-27234	92	75	and	and	CCONJ
fcis-27234	92	76	calculate	calculate	VERB
fcis-27234	92	77	the	the	DET
fcis-27234	92	78	number	number	NOUN
fcis-27234	92	79	of	of	ADP
fcis-27234	92	80	variable	variable	ADJ
fcis-27234	92	81	elements	element	NOUN
fcis-27234	92	82	to	to	PART
fcis-27234	92	83	be	be	AUX
fcis-27234	92	84	optimized	optimize	VERB
fcis-27234	92	85	according	accord	VERB
fcis-27234	92	86	to	to	ADP
fcis-27234	92	87	the	the	DET
fcis-27234	92	88	bp	bp	PROPN
fcis-27234	92	89	neural	neural	ADJ
fcis-27234	92	90	network	network	NOUN
fcis-27234	92	91	structure	structure	NOUN
fcis-27234	92	92	;	;	PUNCT
fcis-27234	92	93	(	(	PUNCT
fcis-27234	92	94	4	4	X
fcis-27234	92	95	)	)	PUNCT
fcis-27234	92	96	pso	pso	NOUN
fcis-27234	92	97	optimization	optimization	NOUN
fcis-27234	92	98	,	,	PUNCT
fcis-27234	92	99	fitness	fitness	NOUN
fcis-27234	92	100	function	function	NOUN
fcis-27234	92	101	is	be	AUX
fcis-27234	92	102	set	set	VERB
fcis-27234	92	103	as	as	ADP
fcis-27234	92	104	the	the	DET
fcis-27234	92	105	mean	mean	ADJ
fcis-27234	92	106	square	square	ADJ
fcis-27234	92	107	error	error	NOUN
fcis-27234	92	108	predicted	predict	VERB
fcis-27234	92	109	by	by	ADP
fcis-27234	92	110	bp	bp	PROPN
fcis-27234	92	111	network	network	PROPN
fcis-27234	92	112	,	,	PUNCT
fcis-27234	92	113	cycle	cycle	NOUN
fcis-27234	92	114	the	the	DET
fcis-27234	92	115	pso	pso	NOUN
fcis-27234	92	116	optimization	optimization	NOUN
fcis-27234	92	117	process	process	NOUN
fcis-27234	92	118	,	,	PUNCT
fcis-27234	92	119	constantly	constantly	ADV
fcis-27234	92	120	update	update	VERB
fcis-27234	92	121	the	the	DET
fcis-27234	92	122	position	position	NOUN
fcis-27234	92	123	of	of	ADP
fcis-27234	92	124	the	the	DET
fcis-27234	92	125	optimal	optimal	ADJ
fcis-27234	92	126	particle	particle	NOUN
fcis-27234	92	127	until	until	ADP
fcis-27234	92	128	the	the	DET
fcis-27234	92	129	maximum	maximum	ADJ
fcis-27234	92	130	number	number	NOUN
fcis-27234	92	131	of	of	ADP
fcis-27234	92	132	iterations	iteration	NOUN
fcis-27234	92	133	,	,	PUNCT
fcis-27234	92	134	and	and	CCONJ
fcis-27234	92	135	terminate	terminate	VERB
fcis-27234	92	136	the	the	DET
fcis-27234	92	137	pso	pso	NOUN
fcis-27234	92	138	algorithm	algorithm	NOUN
fcis-27234	92	139	;	;	PUNCT
fcis-27234	92	140	(	(	PUNCT
fcis-27234	92	141	5	5	X
fcis-27234	92	142	)	)	PUNCT
fcis-27234	92	143	the	the	DET
fcis-27234	92	144	optimal	optimal	ADJ
fcis-27234	92	145	weight	weight	NOUN
fcis-27234	92	146	threshold	threshold	NOUN
fcis-27234	92	147	parameter	parameter	NOUN
fcis-27234	92	148	after	after	SCONJ
fcis-27234	92	149	optimization	optimization	NOUN
fcis-27234	92	150	of	of	ADP
fcis-27234	92	151	the	the	DET
fcis-27234	92	152	pso	pso	NOUN
fcis-27234	92	153	algorithm	algorithm	NOUN
fcis-27234	92	154	is	be	AUX
fcis-27234	92	155	given	give	VERB
fcis-27234	92	156	to	to	ADP
fcis-27234	92	157	the	the	DET
fcis-27234	92	158	bp	bp	PROPN
fcis-27234	92	159	neural	neural	ADJ
fcis-27234	92	160	network	network	NOUN
fcis-27234	92	161	,	,	PUNCT
fcis-27234	92	162	that	that	ADV
fcis-27234	92	163	is	is	ADV
fcis-27234	92	164	,	,	PUNCT
fcis-27234	92	165	the	the	DET
fcis-27234	92	166	optimal	optimal	ADJ
fcis-27234	92	167	pso	pso	NOUN
fcis-27234	92	168	-	-	PUNCT
fcis-27234	92	169	bp	bp	PROPN
fcis-27234	92	170	model	model	NOUN
fcis-27234	92	171	is	be	AUX
fcis-27234	92	172	output	output	NOUN
fcis-27234	92	173	,	,	PUNCT
fcis-27234	92	174	and	and	CCONJ
fcis-27234	92	175	pso	pso	NOUN
fcis-27234	92	176	-	-	PUNCT
fcis-27234	92	177	bp	bp	PROPN
fcis-27234	92	178	is	be	AUX
fcis-27234	92	179	used	use	VERB
fcis-27234	92	180	for	for	ADP
fcis-27234	92	181	training	training	NOUN
fcis-27234	92	182	and	and	CCONJ
fcis-27234	92	183	prediction	prediction	NOUN
fcis-27234	92	184	,	,	PUNCT
fcis-27234	92	185	and	and	CCONJ
fcis-27234	92	186	compared	compare	VERB
fcis-27234	92	187	with	with	ADP
fcis-27234	92	188	the	the	DET
fcis-27234	92	189	bp	bp	PROPN
fcis-27234	92	190	network	network	NOUN
fcis-27234	92	191	before	before	ADP
fcis-27234	92	192	optimization	optimization	NOUN
fcis-27234	92	193	.	.	PUNCT
fcis-27234	93	1	3.3	3.3	NUM
fcis-27234	93	2	.	.	PUNCT
fcis-27234	94	1	model	model	NOUN
fcis-27234	94	2	construction	construction	NOUN
fcis-27234	94	3	in	in	ADP
fcis-27234	94	4	the	the	DET
fcis-27234	94	5	above	above	ADJ
fcis-27234	94	6	discussion	discussion	NOUN
fcis-27234	94	7	of	of	ADP
fcis-27234	94	8	"	"	PUNCT
fcis-27234	94	9	monthly	monthly	ADJ
fcis-27234	94	10	average	average	ADJ
fcis-27234	94	11	area	area	NOUN
fcis-27234	94	12	of	of	ADP
fcis-27234	94	13	sunspot	sunspot	NOUN
fcis-27234	94	14	number	number	NOUN
fcis-27234	94	15	"	"	PUNCT
fcis-27234	94	16	,	,	PUNCT
fcis-27234	94	17	it	it	PRON
fcis-27234	94	18	can	can	AUX
fcis-27234	94	19	be	be	AUX
fcis-27234	94	20	seen	see	VERB
fcis-27234	94	21	that	that	SCONJ
fcis-27234	94	22	there	there	PRON
fcis-27234	94	23	are	be	VERB
fcis-27234	94	24	1794	1794	NUM
fcis-27234	94	25	sets	set	NOUN
fcis-27234	94	26	of	of	ADP
fcis-27234	94	27	data	datum	NOUN
fcis-27234	94	28	through	through	ADP
fcis-27234	94	29	data	datum	NOUN
fcis-27234	94	30	processing	processing	NOUN
fcis-27234	94	31	.	.	PUNCT
fcis-27234	95	1	in	in	ADP
fcis-27234	95	2	the	the	DET
fcis-27234	95	3	time	time	NOUN
fcis-27234	95	4	series	series	PROPN
fcis-27234	95	5	regression	regression	PROPN
fcis-27234	95	6	prediction	prediction	NOUN
fcis-27234	95	7	,	,	PUNCT
fcis-27234	95	8	we	we	PRON
fcis-27234	95	9	classified	classify	VERB
fcis-27234	95	10	1000	1000	NUM
fcis-27234	95	11	sets	set	NOUN
fcis-27234	95	12	of	of	ADP
fcis-27234	95	13	data	datum	NOUN
fcis-27234	95	14	as	as	ADP
fcis-27234	95	15	training	training	NOUN
fcis-27234	95	16	set	set	NOUN
fcis-27234	95	17	,	,	PUNCT
fcis-27234	95	18	794	794	NUM
fcis-27234	95	19	sets	set	NOUN
fcis-27234	95	20	of	of	ADP
fcis-27234	95	21	data	datum	NOUN
fcis-27234	95	22	as	as	ADP
fcis-27234	95	23	test	test	NOUN
fcis-27234	95	24	set	set	NOUN
fcis-27234	95	25	,	,	PUNCT
fcis-27234	95	26	and	and	CCONJ
fcis-27234	95	27	iterated	iterate	VERB
fcis-27234	95	28	15	15	NUM
fcis-27234	95	29	sets	set	NOUN
fcis-27234	95	30	of	of	ADP
fcis-27234	95	31	data	datum	NOUN
fcis-27234	95	32	to	to	PART
fcis-27234	95	33	predict	predict	VERB
fcis-27234	95	34	the	the	DET
fcis-27234	95	35	16th	16th	ADJ
fcis-27234	95	36	data	datum	NOUN
fcis-27234	95	37	,	,	PUNCT
fcis-27234	95	38	and	and	CCONJ
fcis-27234	95	39	so	so	ADV
fcis-27234	95	40	on	on	ADV
fcis-27234	95	41	.	.	PUNCT
fcis-27234	96	1	it	it	PRON
fcis-27234	96	2	was	be	AUX
fcis-27234	96	3	found	find	VERB
fcis-27234	96	4	that	that	SCONJ
fcis-27234	96	5	the	the	DET
fcis-27234	96	6	predicted	predict	VERB
fcis-27234	96	7	data	datum	NOUN
fcis-27234	96	8	and	and	CCONJ
fcis-27234	96	9	frame	frame	NOUN
fcis-27234	96	10	number	number	NOUN
fcis-27234	96	11	data	datum	NOUN
fcis-27234	96	12	had	have	VERB
fcis-27234	96	13	a	a	DET
fcis-27234	96	14	good	good	ADJ
fcis-27234	96	15	fit	fit	NOUN
fcis-27234	96	16	.	.	PUNCT
fcis-27234	97	1	therefore	therefore	ADV
fcis-27234	97	2	,	,	PUNCT
fcis-27234	97	3	we	we	PRON
fcis-27234	97	4	added	add	VERB
fcis-27234	97	5	two	two	NUM
fcis-27234	97	6	more	more	ADJ
fcis-27234	97	7	groups	group	NOUN
fcis-27234	97	8	of	of	ADP
fcis-27234	97	9	data	datum	NOUN
fcis-27234	97	10	numbers	number	NOUN
fcis-27234	97	11	to	to	PART
fcis-27234	97	12	be	be	AUX
fcis-27234	97	13	predicted	predict	VERB
fcis-27234	97	14	in	in	ADP
fcis-27234	97	15	the	the	DET
fcis-27234	97	16	test	test	NOUN
fcis-27234	97	17	set	set	NOUN
fcis-27234	97	18	,	,	PUNCT
fcis-27234	97	19	and	and	CCONJ
fcis-27234	97	20	we	we	PRON
fcis-27234	97	21	can	can	AUX
fcis-27234	97	22	know	know	VERB
fcis-27234	97	23	that	that	SCONJ
fcis-27234	97	24	the	the	DET
fcis-27234	97	25	area	area	NOUN
fcis-27234	97	26	of	of	ADP
fcis-27234	97	27	sunspots	sunspot	NOUN
fcis-27234	97	28	in	in	ADP
fcis-27234	97	29	the	the	DET
fcis-27234	97	30	current	current	ADJ
fcis-27234	97	31	solar	solar	ADJ
fcis-27234	97	32	cycle	cycle	NOUN
fcis-27234	97	33	is	be	AUX
fcis-27234	97	34	1075.56	1075.56	NUM
fcis-27234	97	35	through	through	ADP
fcis-27234	97	36	the	the	DET
fcis-27234	97	37	prediction	prediction	NOUN
fcis-27234	97	38	.	.	PUNCT
fcis-27234	98	1	this	this	PRON
fcis-27234	98	2	gives	give	VERB
fcis-27234	98	3	us	we	PRON
fcis-27234	98	4	a	a	DET
fcis-27234	98	5	total	total	ADJ
fcis-27234	98	6	sunspot	sunspot	NOUN
fcis-27234	98	7	area	area	NOUN
fcis-27234	98	8	of	of	ADP
fcis-27234	98	9	1068.95	1068.95	NUM
fcis-27234	98	10	in	in	ADP
fcis-27234	98	11	the	the	DET
fcis-27234	98	12	next	next	ADJ
fcis-27234	98	13	cycle	cycle	NOUN
fcis-27234	98	14	.	.	PUNCT
fcis-27234	99	1	in	in	ADP
fcis-27234	99	2	the	the	DET
fcis-27234	99	3	above	above	ADJ
fcis-27234	99	4	discussion	discussion	NOUN
fcis-27234	99	5	of	of	ADP
fcis-27234	99	6	"	"	PUNCT
fcis-27234	99	7	total	total	ADJ
fcis-27234	99	8	number	number	NOUN
fcis-27234	99	9	of	of	ADP
fcis-27234	99	10	smoothed	smoothed	ADJ
fcis-27234	99	11	sunspots	sunspot	NOUN
fcis-27234	99	12	per	per	ADP
fcis-27234	99	13	month	month	NOUN
fcis-27234	99	14	"	"	PUNCT
fcis-27234	99	15	,	,	PUNCT
fcis-27234	99	16	we	we	PRON
fcis-27234	99	17	can	can	AUX
fcis-27234	99	18	see	see	VERB
fcis-27234	99	19	a	a	DET
fcis-27234	99	20	total	total	NOUN
fcis-27234	99	21	of	of	ADP
fcis-27234	99	22	3286	3286	NUM
fcis-27234	99	23	sets	set	NOUN
fcis-27234	99	24	of	of	ADP
fcis-27234	99	25	data	datum	NOUN
fcis-27234	99	26	through	through	ADP
fcis-27234	99	27	data	datum	NOUN
fcis-27234	99	28	processing	processing	NOUN
fcis-27234	99	29	.	.	PUNCT
fcis-27234	100	1	in	in	ADP
fcis-27234	100	2	the	the	DET
fcis-27234	100	3	time	time	NOUN
fcis-27234	100	4	series	series	PROPN
fcis-27234	100	5	regression	regression	PROPN
fcis-27234	100	6	prediction	prediction	NOUN
fcis-27234	100	7	,	,	PUNCT
fcis-27234	100	8	we	we	PRON
fcis-27234	100	9	classified	classify	VERB
fcis-27234	100	10	2000	2000	NUM
fcis-27234	100	11	sets	set	NOUN
fcis-27234	100	12	of	of	ADP
fcis-27234	100	13	data	datum	NOUN
fcis-27234	100	14	into	into	ADP
fcis-27234	100	15	the	the	DET
fcis-27234	100	16	training	training	NOUN
fcis-27234	100	17	set	set	NOUN
fcis-27234	100	18	,	,	PUNCT
fcis-27234	100	19	1286	1286	NUM
fcis-27234	100	20	sets	set	NOUN
fcis-27234	100	21	of	of	ADP
fcis-27234	100	22	data	datum	NOUN
fcis-27234	100	23	into	into	ADP
fcis-27234	100	24	the	the	DET
fcis-27234	100	25	test	test	NOUN
fcis-27234	100	26	set	set	NOUN
fcis-27234	100	27	,	,	PUNCT
fcis-27234	100	28	and	and	CCONJ
fcis-27234	100	29	iterated	iterate	VERB
fcis-27234	100	30	15	15	NUM
fcis-27234	100	31	sets	set	NOUN
fcis-27234	100	32	of	of	ADP
fcis-27234	100	33	data	datum	NOUN
fcis-27234	100	34	to	to	PART
fcis-27234	100	35	predict	predict	VERB
fcis-27234	100	36	the	the	DET
fcis-27234	100	37	16th	16th	ADJ
fcis-27234	100	38	data	datum	NOUN
fcis-27234	100	39	,	,	PUNCT
fcis-27234	100	40	and	and	CCONJ
fcis-27234	100	41	so	so	ADV
fcis-27234	100	42	on	on	ADV
fcis-27234	100	43	.	.	PUNCT
fcis-27234	101	1	it	it	PRON
fcis-27234	101	2	was	be	AUX
fcis-27234	101	3	found	find	VERB
fcis-27234	101	4	that	that	SCONJ
fcis-27234	101	5	the	the	DET
fcis-27234	101	6	predicted	predict	VERB
fcis-27234	101	7	data	datum	NOUN
fcis-27234	101	8	and	and	CCONJ
fcis-27234	101	9	frame	frame	NOUN
fcis-27234	101	10	number	number	NOUN
fcis-27234	101	11	data	datum	NOUN
fcis-27234	101	12	had	have	VERB
fcis-27234	101	13	a	a	DET
fcis-27234	101	14	good	good	ADJ
fcis-27234	101	15	fit	fit	NOUN
fcis-27234	101	16	.	.	PUNCT
fcis-27234	102	1	therefore	therefore	ADV
fcis-27234	102	2	,	,	PUNCT
fcis-27234	102	3	we	we	PRON
fcis-27234	102	4	add	add	VERB
fcis-27234	102	5	two	two	NUM
fcis-27234	102	6	more	more	ADJ
fcis-27234	102	7	groups	group	NOUN
fcis-27234	102	8	of	of	ADP
fcis-27234	102	9	data	datum	NOUN
fcis-27234	102	10	numbers	number	NOUN
fcis-27234	102	11	to	to	PART
fcis-27234	102	12	be	be	AUX
fcis-27234	102	13	predicted	predict	VERB
fcis-27234	102	14	in	in	ADP
fcis-27234	102	15	the	the	DET
fcis-27234	102	16	test	test	NOUN
fcis-27234	102	17	set	set	NOUN
fcis-27234	102	18	,	,	PUNCT
fcis-27234	102	19	and	and	CCONJ
fcis-27234	102	20	we	we	PRON
fcis-27234	102	21	can	can	AUX
fcis-27234	102	22	know	know	VERB
fcis-27234	102	23	that	that	SCONJ
fcis-27234	102	24	the	the	DET
fcis-27234	102	25	number	number	NOUN
fcis-27234	102	26	of	of	ADP
fcis-27234	102	27	sunspots	sunspot	NOUN
fcis-27234	102	28	in	in	ADP
fcis-27234	102	29	the	the	DET
fcis-27234	102	30	current	current	ADJ
fcis-27234	102	31	solar	solar	ADJ
fcis-27234	102	32	cycle	cycle	NOUN
fcis-27234	102	33	is	be	AUX
fcis-27234	102	34	133.2	133.2	NUM
fcis-27234	102	35	through	through	ADP
fcis-27234	102	36	prediction	prediction	NOUN
fcis-27234	102	37	.	.	PUNCT
fcis-27234	103	1	this	this	PRON
fcis-27234	103	2	gives	give	VERB
fcis-27234	103	3	us	we	PRON
fcis-27234	103	4	a	a	DET
fcis-27234	103	5	total	total	NOUN
fcis-27234	103	6	of	of	ADP
fcis-27234	103	7	156.3	156.3	NUM
fcis-27234	103	8	sunspots	sunspot	NOUN
fcis-27234	103	9	in	in	ADP
fcis-27234	103	10	the	the	DET
fcis-27234	103	11	next	next	ADJ
fcis-27234	103	12	cycle	cycle	NOUN
fcis-27234	103	13	.	.	PUNCT
fcis-27234	104	1	when	when	SCONJ
fcis-27234	104	2	optimizing	optimize	VERB
fcis-27234	104	3	bp	bp	PROPN
fcis-27234	104	4	neural	neural	ADJ
fcis-27234	104	5	network	network	NOUN
fcis-27234	104	6	for	for	ADP
fcis-27234	104	7	time	time	NOUN
fcis-27234	104	8	series	series	PROPN
fcis-27234	104	9	prediction	prediction	PROPN
fcis-27234	104	10	,	,	PUNCT
fcis-27234	104	11	the	the	DET
fcis-27234	104	12	weight	weight	NOUN
fcis-27234	104	13	and	and	CCONJ
fcis-27234	104	14	threshold	threshold	NOUN
fcis-27234	104	15	of	of	ADP
fcis-27234	104	16	bp	bp	PROPN
fcis-27234	104	17	neural	neural	ADJ
fcis-27234	104	18	network	network	NOUN
fcis-27234	104	19	can	can	AUX
fcis-27234	104	20	be	be	AUX
fcis-27234	104	21	represented	represent	VERB
fcis-27234	104	22	as	as	ADP
fcis-27234	104	23	the	the	DET
fcis-27234	104	24	position	position	NOUN
fcis-27234	104	25	of	of	ADP
fcis-27234	104	26	the	the	DET
fcis-27234	104	27	particle	particle	NOUN
fcis-27234	104	28	,	,	PUNCT
fcis-27234	104	29	and	and	CCONJ
fcis-27234	104	30	mean	mean	VERB
fcis-27234	104	31	square	square	ADJ
fcis-27234	104	32	error	error	NOUN
fcis-27234	104	33	or	or	CCONJ
fcis-27234	104	34	other	other	ADJ
fcis-27234	104	35	appropriate	appropriate	ADJ
fcis-27234	104	36	evaluation	evaluation	NOUN
fcis-27234	104	37	index	index	NOUN
fcis-27234	104	38	can	can	AUX
fcis-27234	104	39	be	be	AUX
fcis-27234	104	40	used	use	VERB
fcis-27234	104	41	as	as	ADP
fcis-27234	104	42	fitness	fitness	NOUN
fcis-27234	104	43	function	function	NOUN
fcis-27234	104	44	.	.	PUNCT
fcis-27234	105	1	by	by	ADP
fcis-27234	105	2	constantly	constantly	ADV
fcis-27234	105	3	updating	update	VERB
fcis-27234	105	4	the	the	DET
fcis-27234	105	5	position	position	NOUN
fcis-27234	105	6	and	and	CCONJ
fcis-27234	105	7	velocity	velocity	NOUN
fcis-27234	105	8	of	of	ADP
fcis-27234	105	9	the	the	DET
fcis-27234	105	10	particles	particle	NOUN
fcis-27234	105	11	,	,	PUNCT
fcis-27234	105	12	the	the	DET
fcis-27234	105	13	prediction	prediction	NOUN
fcis-27234	105	14	error	error	NOUN
fcis-27234	105	15	of	of	ADP
fcis-27234	105	16	bp	bp	PROPN
fcis-27234	105	17	neural	neural	ADJ
fcis-27234	105	18	network	network	NOUN
fcis-27234	105	19	is	be	AUX
fcis-27234	105	20	gradually	gradually	ADV
fcis-27234	105	21	reduced	reduce	VERB
fcis-27234	105	22	,	,	PUNCT
fcis-27234	105	23	and	and	CCONJ
fcis-27234	105	24	better	well	ADJ
fcis-27234	105	25	prediction	prediction	NOUN
fcis-27234	105	26	results	result	NOUN
fcis-27234	105	27	are	be	AUX
fcis-27234	105	28	obtained	obtain	VERB
fcis-27234	105	29	.	.	PUNCT
fcis-27234	106	1	the	the	DET
fcis-27234	106	2	pso	pso	NOUN
fcis-27234	106	3	is	be	AUX
fcis-27234	106	4	initialized	initialize	VERB
fcis-27234	106	5	to	to	ADP
fcis-27234	106	6	a	a	DET
fcis-27234	106	7	group	group	NOUN
fcis-27234	106	8	of	of	ADP
fcis-27234	106	9	random	random	ADJ
fcis-27234	106	10	particles	particle	NOUN
fcis-27234	106	11	(	(	PUNCT
fcis-27234	106	12	random	random	ADJ
fcis-27234	106	13	solutions	solution	NOUN
fcis-27234	106	14	)	)	PUNCT
fcis-27234	106	15	.	.	PUNCT
fcis-27234	107	1	the	the	DET
fcis-27234	107	2	optimal	optimal	ADJ
fcis-27234	107	3	solution	solution	NOUN
fcis-27234	107	4	is	be	AUX
fcis-27234	107	5	then	then	ADV
fcis-27234	107	6	found	find	VERB
fcis-27234	107	7	through	through	ADP
fcis-27234	107	8	iteration	iteration	NOUN
fcis-27234	107	9	.	.	PUNCT
fcis-27234	108	1	in	in	ADP
fcis-27234	108	2	each	each	DET
fcis-27234	108	3	iteration	iteration	NOUN
fcis-27234	108	4	,	,	PUNCT
fcis-27234	108	5	the	the	DET
fcis-27234	108	6	particle	particle	NOUN
fcis-27234	108	7	updates	update	VERB
fcis-27234	108	8	itself	itself	PRON
fcis-27234	108	9	by	by	ADP
fcis-27234	108	10	tracking	track	VERB
fcis-27234	108	11	two	two	NUM
fcis-27234	108	12	"	"	PUNCT
fcis-27234	108	13	extreme	extreme	ADJ
fcis-27234	108	14	values	value	NOUN
fcis-27234	108	15	"	"	PUNCT
fcis-27234	108	16	(	(	PUNCT
fcis-27234	108	17	pbest	pb	ADJ
fcis-27234	108	18	,	,	PUNCT
fcis-27234	108	19	gbest	gbest	NOUN
fcis-27234	108	20	)	)	PUNCT
fcis-27234	108	21	.	.	PUNCT
fcis-27234	109	1	after	after	ADP
fcis-27234	109	2	finding	find	VERB
fcis-27234	109	3	these	these	DET
fcis-27234	109	4	two	two	NUM
fcis-27234	109	5	best	good	ADJ
fcis-27234	109	6	values	value	NOUN
fcis-27234	109	7	,	,	PUNCT
fcis-27234	109	8	the	the	DET
fcis-27234	109	9	particle	particle	NOUN
fcis-27234	109	10	updates	update	VERB
fcis-27234	109	11	its	its	PRON
fcis-27234	109	12	speed	speed	NOUN
fcis-27234	109	13	and	and	CCONJ
fcis-27234	109	14	position	position	NOUN
fcis-27234	109	15	by	by	ADP
fcis-27234	109	16	the	the	DET
fcis-27234	109	17	formula	formula	NOUN
fcis-27234	109	18	.	.	PUNCT
fcis-27234	110	1	3.4	3.4	NUM
fcis-27234	110	2	.	.	PUNCT
fcis-27234	110	3	model	model	NOUN
fcis-27234	110	4	reliability	reliability	NOUN
fcis-27234	110	5	predict	predict	VERB
fcis-27234	110	6	the	the	DET
fcis-27234	110	7	accuracy	accuracy	NOUN
fcis-27234	110	8	rate	rate	NOUN
fcis-27234	110	9	through	through	ADP
fcis-27234	110	10	various	various	ADJ
fcis-27234	110	11	error	error	NOUN
fcis-27234	110	12	indexes	index	NOUN
fcis-27234	110	13	of	of	ADP
fcis-27234	110	14	bp	bp	PROPN
fcis-27234	110	15	and	and	CCONJ
fcis-27234	110	16	pso	pso	NOUN
fcis-27234	110	17	-	-	PUNCT
fcis-27234	110	18	bp	bp	PROPN
fcis-27234	110	19	.	.	PUNCT
fcis-27234	110	20	table	table	NOUN
fcis-27234	110	21	2	2	NUM
fcis-27234	110	22	.	.	X
fcis-27234	110	23	error	error	NOUN
fcis-27234	110	24	indexes	index	NOUN
fcis-27234	110	25	of	of	ADP
fcis-27234	110	26	bp	bp	PROPN
fcis-27234	110	27	and	and	CCONJ
fcis-27234	110	28	pso	pso	NOUN
fcis-27234	110	29	-	-	PUNCT
fcis-27234	110	30	bp	bp	PROPN
fcis-27234	110	31	mean	mean	VERB
fcis-27234	110	32	absolute	absolute	ADJ
fcis-27234	110	33	error	error	NOUN
fcis-27234	110	34	mae	mae	PROPN
fcis-27234	110	35	mean	mean	PROPN
fcis-27234	110	36	square	square	ADJ
fcis-27234	110	37	error	error	NOUN
fcis-27234	110	38	mse	mse	NOUN
fcis-27234	110	39	mean	mean	VERB
fcis-27234	110	40	square	square	NOUN
fcis-27234	110	41	error	error	NOUN
fcis-27234	110	42	root	root	NOUN
fcis-27234	110	43	rmse	rmse	NOUN
fcis-27234	110	44	mean	mean	VERB
fcis-27234	110	45	absolute	absolute	ADJ
fcis-27234	110	46	percentage	percentage	NOUN
fcis-27234	110	47	error	error	NOUN
fcis-27234	110	48	mape	mape	NOUN
fcis-27234	110	49	bp	bp	PROPN
fcis-27234	110	50	forecast	forecast	PROPN
fcis-27234	110	51	accuracy	accuracy	PROPN
fcis-27234	110	52	pso	pso	NOUN
fcis-27234	110	53	-	-	PUNCT
fcis-27234	110	54	bp	bp	PROPN
fcis-27234	110	55	prediction	prediction	NOUN
fcis-27234	110	56	accuracy	accuracy	NOUN
fcis-27234	110	57	bp	bp	PROPN
fcis-27234	110	58	neural	neural	ADJ
fcis-27234	110	59	network	network	NOUN
fcis-27234	110	60	3.9539	3.9539	NUM
fcis-27234	110	61	22.2639	22.2639	NUM
fcis-27234	110	62	4.7185	4.7185	NUM
fcis-27234	110	63	31.7068	31.7068	NUM
fcis-27234	110	64	%	%	NOUN
fcis-27234	110	65	/	/	SYM
fcis-27234	110	66	/	/	SYM
fcis-27234	110	67	pso	pso	NOUN
fcis-27234	110	68	optimizes	optimize	VERB
fcis-27234	110	69	bp	bp	PROPN
fcis-27234	110	70	neural	neural	ADJ
fcis-27234	110	71	network	network	NOUN
fcis-27234	110	72	1.2073	1.2073	NUM
fcis-27234	110	73	2.4017	2.4017	NUM
fcis-27234	110	74	1.5498	1.5498	NUM
fcis-27234	110	75	8.2818	8.2818	NUM
fcis-27234	110	76	%	%	NOUN
fcis-27234	110	77	68.2932	68.2932	NUM
fcis-27234	110	78	%	%	NOUN
fcis-27234	110	79	91.7182	91.7182	NUM
fcis-27234	110	80	%	%	NOUN
fcis-27234	110	81	as	as	SCONJ
fcis-27234	110	82	can	can	AUX
fcis-27234	110	83	be	be	AUX
fcis-27234	110	84	seen	see	VERB
fcis-27234	110	85	from	from	ADP
fcis-27234	110	86	table	table	NOUN
fcis-27234	110	87	2	2	NUM
fcis-27234	110	88	,	,	PUNCT
fcis-27234	110	89	the	the	DET
fcis-27234	110	90	prediction	prediction	NOUN
fcis-27234	110	91	accuracy	accuracy	NOUN
fcis-27234	110	92	of	of	ADP
fcis-27234	110	93	the	the	DET
fcis-27234	110	94	model	model	NOUN
fcis-27234	110	95	can	can	AUX
fcis-27234	110	96	be	be	AUX
fcis-27234	110	97	indirectly	indirectly	ADV
fcis-27234	110	98	predicted	predict	VERB
fcis-27234	110	99	by	by	ADP
fcis-27234	110	100	the	the	DET
fcis-27234	110	101	various	various	ADJ
fcis-27234	110	102	error	error	NOUN
fcis-27234	110	103	metrics	metric	NOUN
fcis-27234	110	104	of	of	ADP
fcis-27234	110	105	bp	bp	PROPN
fcis-27234	110	106	and	and	CCONJ
fcis-27234	110	107	pso	pso	NOUN
fcis-27234	110	108	-	-	PUNCT
fcis-27234	110	109	bp	bp	PROPN
fcis-27234	110	110	.	.	PUNCT
fcis-27234	111	1	we	we	PRON
fcis-27234	111	2	found	find	VERB
fcis-27234	111	3	that	that	SCONJ
fcis-27234	111	4	:	:	PUNCT
fcis-27234	111	5	comparing	compare	VERB
fcis-27234	111	6	the	the	DET
fcis-27234	111	7	error	error	NOUN
fcis-27234	111	8	metrics	metric	NOUN
fcis-27234	111	9	of	of	ADP
fcis-27234	111	10	bp	bp	PROPN
fcis-27234	111	11	and	and	CCONJ
fcis-27234	111	12	pso	pso	NOUN
fcis-27234	111	13	-	-	PUNCT
fcis-27234	111	14	bp	bp	PROPN
fcis-27234	111	15	,	,	PUNCT
fcis-27234	111	16	including	include	VERB
fcis-27234	111	17	mean	mean	ADJ
fcis-27234	111	18	square	square	ADJ
fcis-27234	111	19	error	error	NOUN
fcis-27234	111	20	(	(	PUNCT
fcis-27234	111	21	mse	mse	NOUN
fcis-27234	111	22	)	)	PUNCT
fcis-27234	111	23	,	,	PUNCT
fcis-27234	111	24	root	root	NOUN
fcis-27234	111	25	mean	mean	VERB
fcis-27234	111	26	square	square	ADJ
fcis-27234	111	27	error	error	NOUN
fcis-27234	111	28	(	(	PUNCT
fcis-27234	111	29	rmse	rmse	NOUN
fcis-27234	111	30	)	)	PUNCT
fcis-27234	111	31	,	,	PUNCT
fcis-27234	111	32	and	and	CCONJ
fcis-27234	111	33	mean	mean	VERB
fcis-27234	111	34	absolute	absolute	ADJ
fcis-27234	111	35	error	error	NOUN
fcis-27234	111	36	(	(	PUNCT
fcis-27234	111	37	mae	mae	PROPN
fcis-27234	111	38	)	)	PUNCT
fcis-27234	111	39	,	,	PUNCT
fcis-27234	111	40	etc	etc	X
fcis-27234	111	41	.	.	X
fcis-27234	112	1	the	the	DET
fcis-27234	112	2	error	error	NOUN
fcis-27234	112	3	metrics	metric	NOUN
fcis-27234	112	4	of	of	ADP
fcis-27234	112	5	pso	pso	NOUN
fcis-27234	112	6	-	-	PUNCT
fcis-27234	112	7	bp	bp	PROPN
fcis-27234	112	8	are	be	AUX
fcis-27234	112	9	significantly	significantly	ADV
fcis-27234	112	10	lower	low	ADJ
fcis-27234	112	11	than	than	ADP
fcis-27234	112	12	those	those	PRON
fcis-27234	112	13	of	of	ADP
fcis-27234	112	14	bp	bp	PROPN
fcis-27234	112	15	.	.	PUNCT
fcis-27234	113	1	figure	figure	VERB
fcis-27234	113	2	4	4	NUM
fcis-27234	113	3	.	.	PUNCT
fcis-27234	113	4	relationship	relationship	NOUN
fcis-27234	113	5	between	between	ADP
fcis-27234	113	6	model	model	NOUN
fcis-27234	113	7	iteration	iteration	NOUN
fcis-27234	113	8	error	error	NOUN
fcis-27234	113	9	and	and	CCONJ
fcis-27234	113	10	particle	particle	NOUN
fcis-27234	113	11	number	number	NOUN
fcis-27234	113	12	regression	regression	NOUN
fcis-27234	113	13	graphs	graph	NOUN
fcis-27234	113	14	of	of	ADP
fcis-27234	113	15	bp	bp	PROPN
fcis-27234	113	16	and	and	CCONJ
fcis-27234	113	17	pso	pso	NOUN
fcis-27234	113	18	-	-	PUNCT
fcis-27234	113	19	bp	bp	PROPN
fcis-27234	113	20	models	model	NOUN
fcis-27234	113	21	,	,	PUNCT
fcis-27234	113	22	using	use	VERB
fcis-27234	113	23	the	the	DET
fcis-27234	113	24	total	total	ADJ
fcis-27234	113	25	number	number	NOUN
fcis-27234	113	26	of	of	ADP
fcis-27234	113	27	predicted	predict	VERB
fcis-27234	113	28	sunspots	sunspot	NOUN
fcis-27234	113	29	as	as	ADP
fcis-27234	113	30	an	an	DET
fcis-27234	113	31	example	example	NOUN
fcis-27234	113	32	.	.	PUNCT
fcis-27234	114	1	the	the	DET
fcis-27234	114	2	figure	figure	NOUN
fcis-27234	114	3	4	4	NUM
fcis-27234	114	4	shows	show	VERB
fcis-27234	114	5	the	the	DET
fcis-27234	114	6	relationship	relationship	NOUN
fcis-27234	114	7	between	between	ADP
fcis-27234	114	8	model	model	NOUN
fcis-27234	114	9	iteration	iteration	NOUN
fcis-27234	114	10	error	error	NOUN
fcis-27234	114	11	and	and	CCONJ
fcis-27234	114	12	particle	particle	NOUN
fcis-27234	114	13	number	number	NOUN
fcis-27234	114	14	,	,	PUNCT
fcis-27234	114	15	with	with	ADP
fcis-27234	114	16	the	the	DET
fcis-27234	114	17	horizontal	horizontal	ADJ
fcis-27234	114	18	coordinate	coordinate	NOUN
fcis-27234	114	19	representing	represent	VERB
fcis-27234	114	20	particle	particle	NOUN
fcis-27234	114	21	number	number	NOUN
fcis-27234	114	22	and	and	CCONJ
fcis-27234	114	23	the	the	DET
fcis-27234	114	24	vertical	vertical	ADJ
fcis-27234	114	25	coordinate	coordinate	NOUN
fcis-27234	114	26	representing	represent	VERB
fcis-27234	114	27	model	model	NOUN
fcis-27234	114	28	iteration	iteration	NOUN
fcis-27234	114	29	error	error	NOUN
fcis-27234	114	30	.	.	PUNCT
fcis-27234	115	1	as	as	SCONJ
fcis-27234	115	2	can	can	AUX
fcis-27234	115	3	be	be	AUX
fcis-27234	115	4	seen	see	VERB
fcis-27234	115	5	from	from	ADP
fcis-27234	115	6	the	the	DET
fcis-27234	115	7	figure	figure	NOUN
fcis-27234	115	8	,	,	PUNCT
fcis-27234	115	9	as	as	ADP
fcis-27234	115	10	the	the	DET
fcis-27234	115	11	number	number	NOUN
fcis-27234	115	12	of	of	ADP
fcis-27234	115	13	particles	particle	NOUN
fcis-27234	115	14	increases	increase	NOUN
fcis-27234	115	15	,	,	PUNCT
fcis-27234	115	16	the	the	DET
fcis-27234	115	17	iteration	iteration	NOUN
fcis-27234	115	18	errors	error	NOUN
fcis-27234	115	19	of	of	ADP
fcis-27234	115	20	the	the	DET
fcis-27234	115	21	model	model	NOUN
fcis-27234	115	22	show	show	VERB
fcis-27234	115	23	a	a	DET
fcis-27234	115	24	trend	trend	NOUN
fcis-27234	115	25	of	of	ADP
fcis-27234	115	26	decreasing	decrease	VERB
fcis-27234	115	27	first	first	ADV
fcis-27234	115	28	and	and	CCONJ
fcis-27234	115	29	then	then	ADV
fcis-27234	115	30	increasing	increase	VERB
fcis-27234	115	31	.	.	PUNCT
fcis-27234	116	1	the	the	DET
fcis-27234	116	2	position	position	NOUN
fcis-27234	116	3	and	and	CCONJ
fcis-27234	116	4	velocity	velocity	NOUN
fcis-27234	116	5	changes	change	NOUN
fcis-27234	116	6	of	of	ADP
fcis-27234	116	7	particle	particle	NOUN
fcis-27234	116	8	swarm	swarm	NOUN
fcis-27234	116	9	during	during	ADP
fcis-27234	116	10	pso	pso	NOUN
fcis-27234	116	11	-	-	PUNCT
fcis-27234	116	12	bp	bp	PROPN
fcis-27234	116	13	optimization	optimization	NOUN
fcis-27234	116	14	were	be	AUX
fcis-27234	116	15	observed	observe	VERB
fcis-27234	116	16	.	.	PUNCT
fcis-27234	117	1	it	it	PRON
fcis-27234	117	2	is	be	AUX
fcis-27234	117	3	found	find	VERB
fcis-27234	117	4	that	that	SCONJ
fcis-27234	117	5	the	the	DET
fcis-27234	117	6	particle	particle	NOUN
fcis-27234	117	7	swarm	swarm	NOUN
fcis-27234	117	8	can	can	AUX
fcis-27234	117	9	find	find	VERB
fcis-27234	117	10	a	a	DET
fcis-27234	117	11	better	well	ADJ
fcis-27234	117	12	solution	solution	NOUN
fcis-27234	117	13	,	,	PUNCT
fcis-27234	117	14	and	and	CCONJ
fcis-27234	117	15	the	the	DET
fcis-27234	117	16	position	position	NOUN
fcis-27234	117	17	and	and	CCONJ
fcis-27234	117	18	velocity	velocity	NOUN
fcis-27234	117	19	change	change	NOUN
fcis-27234	117	20	trend	trend	NOUN
fcis-27234	117	21	is	be	AUX
fcis-27234	117	22	relatively	relatively	ADV
fcis-27234	117	23	stable	stable	ADJ
fcis-27234	117	24	during	during	ADP
fcis-27234	117	25	the	the	DET
fcis-27234	117	26	iteration	iteration	NOUN
fcis-27234	117	27	process	process	NOUN
fcis-27234	117	28	,	,	PUNCT
fcis-27234	117	29	so	so	SCONJ
fcis-27234	117	30	it	it	PRON
fcis-27234	117	31	can	can	AUX
fcis-27234	117	32	be	be	AUX
fcis-27234	117	33	considered	consider	VERB
fcis-27234	117	34	that	that	SCONJ
fcis-27234	117	35	the	the	DET
fcis-27234	117	36	prediction	prediction	NOUN
fcis-27234	117	37	accuracy	accuracy	NOUN
fcis-27234	117	38	of	of	ADP
fcis-27234	117	39	the	the	DET
fcis-27234	117	40	model	model	NOUN
fcis-27234	117	41	may	may	AUX
fcis-27234	117	42	be	be	AUX
fcis-27234	117	43	higher	high	ADJ
fcis-27234	117	44	.	.	PUNCT
fcis-27234	118	1	it	it	PRON
fcis-27234	118	2	can	can	AUX
fcis-27234	118	3	be	be	AUX
fcis-27234	118	4	seen	see	VERB
fcis-27234	118	5	from	from	ADP
fcis-27234	118	6	the	the	DET
fcis-27234	118	7	above	above	ADV
fcis-27234	118	8	that	that	SCONJ
fcis-27234	118	9	the	the	DET
fcis-27234	118	10	model	model	NOUN
fcis-27234	118	11	has	have	VERB
fcis-27234	118	12	high	high	ADJ
fcis-27234	118	13	accuracy	accuracy	NOUN
fcis-27234	118	14	and	and	CCONJ
fcis-27234	118	15	strong	strong	ADJ
fcis-27234	118	16	reliability	reliability	NOUN
fcis-27234	118	17	.	.	PUNCT
fcis-27234	119	1	in	in	ADP
fcis-27234	119	2	order	order	NOUN
fcis-27234	119	3	to	to	PART
fcis-27234	119	4	predict	predict	VERB
fcis-27234	119	5	the	the	DET
fcis-27234	119	6	number	number	NOUN
fcis-27234	119	7	and	and	CCONJ
fcis-27234	119	8	area	area	NOUN
fcis-27234	119	9	of	of	ADP
fcis-27234	119	10	sunspots	sunspot	NOUN
fcis-27234	119	11	in	in	ADP
fcis-27234	119	12	the	the	DET
fcis-27234	119	13	current	current	ADJ
fcis-27234	119	14	and	and	CCONJ
fcis-27234	119	15	next	next	ADJ
fcis-27234	119	16	solar	solar	ADJ
fcis-27234	119	17	cycle	cycle	NOUN
fcis-27234	119	18	.	.	PUNCT
fcis-27234	120	1	we	we	PRON
fcis-27234	120	2	introduce	introduce	VERB
fcis-27234	120	3	fly	fly	VERB
fcis-27234	120	4	to	to	PART
fcis-27234	120	5	analyze	analyze	VERB
fcis-27234	120	6	periodicity	periodicity	NOUN
fcis-27234	120	7	and	and	CCONJ
fcis-27234	120	8	find	find	VERB
fcis-27234	120	9	that	that	SCONJ
fcis-27234	120	10	the	the	DET
fcis-27234	120	11	number	number	NOUN
fcis-27234	120	12	and	and	CCONJ
fcis-27234	120	13	area	area	NOUN
fcis-27234	120	14	of	of	ADP
fcis-27234	120	15	sunspots	sunspot	NOUN
fcis-27234	120	16	have	have	VERB
fcis-27234	120	17	certain	certain	ADJ
fcis-27234	120	18	periodic	periodic	ADJ
fcis-27234	120	19	changes	change	NOUN
fcis-27234	120	20	.	.	PUNCT
fcis-27234	121	1	combined	combine	VERB
fcis-27234	121	2	with	with	ADP
fcis-27234	121	3	the	the	DET
fcis-27234	121	4	time	time	NOUN
fcis-27234	121	5	series	series	PROPN
fcis-27234	121	6	prediction	prediction	NOUN
fcis-27234	121	7	model	model	NOUN
fcis-27234	121	8	based	base	VERB
fcis-27234	121	9	on	on	ADP
fcis-27234	121	10	bp	bp	PROPN
fcis-27234	121	11	neural	neural	ADJ
fcis-27234	121	12	network	network	NOUN
fcis-27234	121	13	of	of	ADP
fcis-27234	121	14	particle	particle	NOUN
fcis-27234	121	15	swarm	swarm	NOUN
fcis-27234	121	16	optimization	optimization	NOUN
fcis-27234	121	17	,	,	PUNCT
fcis-27234	121	18	a	a	DET
fcis-27234	121	19	time	time	NOUN
fcis-27234	121	20	prediction	prediction	NOUN
fcis-27234	121	21	trend	trend	NOUN
fcis-27234	121	22	graph	graph	NOUN
fcis-27234	121	23	is	be	AUX
fcis-27234	121	24	established	establish	VERB
fcis-27234	121	25	.	.	PUNCT
fcis-27234	122	1	analyze	analyze	VERB
fcis-27234	122	2	and	and	CCONJ
fcis-27234	122	3	process	process	NOUN
fcis-27234	122	4	"	"	PUNCT
fcis-27234	122	5	monthly	monthly	ADJ
fcis-27234	122	6	average	average	ADJ
fcis-27234	122	7	sunspot	sunspot	NOUN
fcis-27234	122	8	area	area	NOUN
fcis-27234	122	9	"	"	PUNCT
fcis-27234	122	10	and	and	CCONJ
fcis-27234	122	11	"	"	PUNCT
fcis-27234	122	12	smooth	smooth	ADJ
fcis-27234	122	13	monthly	monthly	ADJ
fcis-27234	122	14	sunspot	sunspot	NOUN
fcis-27234	122	15	number	number	NOUN
fcis-27234	122	16	"	"	PUNCT
fcis-27234	122	17	,	,	PUNCT
fcis-27234	122	18	and	and	CCONJ
fcis-27234	122	19	use	use	VERB
fcis-27234	122	20	15	15	NUM
fcis-27234	122	21	sets	set	NOUN
fcis-27234	122	22	of	of	ADP
fcis-27234	122	23	data	datum	NOUN
fcis-27234	122	24	as	as	ADP
fcis-27234	122	25	an	an	DET
fcis-27234	122	26	iterative	iterative	NOUN
fcis-27234	122	27	unit	unit	NOUN
fcis-27234	122	28	to	to	PART
fcis-27234	122	29	predict	predict	VERB
fcis-27234	122	30	the	the	DET
fcis-27234	122	31	next	next	ADJ
fcis-27234	122	32	set	set	NOUN
fcis-27234	122	33	of	of	ADP
fcis-27234	122	34	data	datum	NOUN
fcis-27234	122	35	for	for	ADP
fcis-27234	122	36	iterative	iterative	NOUN
fcis-27234	122	37	training	training	NOUN
fcis-27234	122	38	.	.	PUNCT
fcis-27234	123	1	the	the	DET
fcis-27234	123	2	two	two	NUM
fcis-27234	123	3	data	data	NOUN
fcis-27234	123	4	sets	set	NOUN
fcis-27234	123	5	are	be	AUX
fcis-27234	123	6	divided	divide	VERB
fcis-27234	123	7	into	into	ADP
fcis-27234	123	8	training	training	NOUN
fcis-27234	123	9	set	set	NOUN
fcis-27234	123	10	and	and	CCONJ
fcis-27234	123	11	test	test	NOUN
fcis-27234	123	12	set	set	VERB
fcis-27234	123	13	in	in	ADP
fcis-27234	123	14	turn	turn	NOUN
fcis-27234	123	15	.	.	PUNCT
fcis-27234	124	1	according	accord	VERB
fcis-27234	124	2	to	to	ADP
fcis-27234	124	3	the	the	DET
fcis-27234	124	4	fitting	fitting	ADJ
fcis-27234	124	5	trend	trend	NOUN
fcis-27234	124	6	of	of	ADP
fcis-27234	124	7	the	the	DET
fcis-27234	124	8	54	54	NUM
fcis-27234	124	9	training	training	NOUN
fcis-27234	124	10	set	set	NOUN
fcis-27234	124	11	,	,	PUNCT
fcis-27234	124	12	it	it	PRON
fcis-27234	124	13	is	be	AUX
fcis-27234	124	14	found	find	VERB
fcis-27234	124	15	that	that	SCONJ
fcis-27234	124	16	the	the	DET
fcis-27234	124	17	real	real	ADJ
fcis-27234	124	18	value	value	NOUN
fcis-27234	124	19	and	and	CCONJ
fcis-27234	124	20	the	the	DET
fcis-27234	124	21	predicted	predict	VERB
fcis-27234	124	22	value	value	NOUN
fcis-27234	124	23	have	have	VERB
fcis-27234	124	24	a	a	DET
fcis-27234	124	25	high	high	ADJ
fcis-27234	124	26	coincidence	coincidence	NOUN
fcis-27234	124	27	degree	degree	NOUN
fcis-27234	124	28	.	.	PUNCT
fcis-27234	125	1	therefore	therefore	ADV
fcis-27234	125	2	,	,	PUNCT
fcis-27234	125	3	the	the	DET
fcis-27234	125	4	number	number	NOUN
fcis-27234	125	5	and	and	CCONJ
fcis-27234	125	6	area	area	NOUN
fcis-27234	125	7	of	of	ADP
fcis-27234	125	8	sunspots	sunspot	NOUN
fcis-27234	125	9	in	in	ADP
fcis-27234	125	10	the	the	DET
fcis-27234	125	11	current	current	ADJ
fcis-27234	125	12	and	and	CCONJ
fcis-27234	125	13	next	next	ADJ
fcis-27234	125	14	solar	solar	ADJ
fcis-27234	125	15	cycle	cycle	NOUN
fcis-27234	125	16	were	be	AUX
fcis-27234	125	17	obtained	obtain	VERB
fcis-27234	125	18	by	by	ADP
fcis-27234	125	19	fitting	fit	VERB
fcis-27234	125	20	the	the	DET
fcis-27234	125	21	test	test	NOUN
fcis-27234	125	22	set	set	VERB
fcis-27234	125	23	and	and	CCONJ
fcis-27234	125	24	particle	particle	NOUN
fcis-27234	125	25	swarm	swarm	NOUN
fcis-27234	125	26	optimization	optimization	NOUN
fcis-27234	125	27	was	be	AUX
fcis-27234	125	28	introduced	introduce	VERB
fcis-27234	125	29	to	to	PART
fcis-27234	125	30	optimize	optimize	VERB
fcis-27234	125	31	the	the	DET
fcis-27234	125	32	algorithm	algorithm	NOUN
fcis-27234	125	33	.	.	PUNCT
fcis-27234	126	1	in	in	ADP
fcis-27234	126	2	order	order	NOUN
fcis-27234	126	3	to	to	PART
fcis-27234	126	4	test	test	VERB
fcis-27234	126	5	the	the	DET
fcis-27234	126	6	reliability	reliability	NOUN
fcis-27234	126	7	of	of	ADP
fcis-27234	126	8	the	the	DET
fcis-27234	126	9	model	model	NOUN
fcis-27234	126	10	,	,	PUNCT
fcis-27234	126	11	the	the	DET
fcis-27234	126	12	prediction	prediction	NOUN
fcis-27234	126	13	accuracy	accuracy	NOUN
fcis-27234	126	14	of	of	ADP
fcis-27234	126	15	bp	bp	PROPN
fcis-27234	126	16	neural	neural	ADJ
fcis-27234	126	17	network	network	NOUN
fcis-27234	126	18	was	be	AUX
fcis-27234	126	19	found	find	VERB
fcis-27234	126	20	to	to	PART
fcis-27234	126	21	be	be	AUX
fcis-27234	126	22	68.2932	68.2932	NUM
fcis-27234	126	23	%	%	NOUN
fcis-27234	126	24	through	through	ADP
fcis-27234	126	25	the	the	DET
fcis-27234	126	26	prediction	prediction	NOUN
fcis-27234	126	27	and	and	CCONJ
fcis-27234	126	28	inspection	inspection	NOUN
fcis-27234	126	29	of	of	ADP
fcis-27234	126	30	bp	bp	PROPN
fcis-27234	126	31	neural	neural	ADJ
fcis-27234	126	32	network	network	NOUN
fcis-27234	126	33	.	.	PUNCT
fcis-27234	127	1	and	and	CCONJ
fcis-27234	127	2	the	the	DET
fcis-27234	127	3	accuracy	accuracy	NOUN
fcis-27234	127	4	of	of	ADP
fcis-27234	127	5	bp	bp	PROPN
fcis-27234	127	6	neural	neural	ADJ
fcis-27234	127	7	network	network	PROPN
fcis-27234	127	8	time	time	PROPN
fcis-27234	127	9	series	series	PROPN
fcis-27234	127	10	prediction	prediction	NOUN
fcis-27234	127	11	model	model	NOUN
fcis-27234	127	12	based	base	VERB
fcis-27234	127	13	on	on	ADP
fcis-27234	127	14	particle	particle	NOUN
fcis-27234	127	15	swarm	swarm	NOUN
fcis-27234	127	16	optimization	optimization	NOUN
fcis-27234	127	17	is	be	AUX
fcis-27234	127	18	91.7182	91.7182	NUM
fcis-27234	127	19	%	%	NOUN
fcis-27234	127	20	,	,	PUNCT
fcis-27234	127	21	and	and	CCONJ
fcis-27234	127	22	its	its	PRON
fcis-27234	127	23	model	model	NOUN
fcis-27234	127	24	has	have	VERB
fcis-27234	127	25	certain	certain	ADJ
fcis-27234	127	26	reliability	reliability	NOUN
fcis-27234	127	27	.	.	PUNCT
fcis-27234	128	1	4	4	X
fcis-27234	128	2	.	.	X
fcis-27234	128	3	conclusion	conclusion	NOUN
fcis-27234	128	4	in	in	ADP
fcis-27234	128	5	this	this	DET
fcis-27234	128	6	study	study	NOUN
fcis-27234	128	7	,	,	PUNCT
fcis-27234	128	8	pearson	pearson	PROPN
fcis-27234	128	9	correlation	correlation	NOUN
fcis-27234	128	10	coefficient	coefficient	NOUN
fcis-27234	128	11	analysis	analysis	NOUN
fcis-27234	128	12	,	,	PUNCT
fcis-27234	128	13	adaptive	adaptive	ADJ
fcis-27234	128	14	multivariate	multivariate	NOUN
fcis-27234	128	15	nonlinear	nonlinear	ADJ
fcis-27234	128	16	regression	regression	NOUN
fcis-27234	128	17	-	-	PUNCT
fcis-27234	128	18	bp	bp	PROPN
fcis-27234	128	19	neural	neural	ADJ
fcis-27234	128	20	network	network	NOUN
fcis-27234	128	21	model	model	NOUN
fcis-27234	128	22	and	and	CCONJ
fcis-27234	128	23	particle	particle	NOUN
fcis-27234	128	24	swarm	swarm	NOUN
fcis-27234	128	25	optimization	optimization	PROPN
fcis-27234	128	26	bp	bp	PROPN
fcis-27234	128	27	neural	neural	ADJ
fcis-27234	128	28	network	network	NOUN
fcis-27234	128	29	were	be	AUX
fcis-27234	128	30	used	use	VERB
fcis-27234	128	31	to	to	PART
fcis-27234	128	32	construct	construct	VERB
fcis-27234	128	33	a	a	DET
fcis-27234	128	34	comprehensive	comprehensive	ADJ
fcis-27234	128	35	prediction	prediction	NOUN
fcis-27234	128	36	model	model	NOUN
fcis-27234	128	37	of	of	ADP
fcis-27234	128	38	sunspot	sunspot	NOUN
fcis-27234	128	39	activity	activity	NOUN
fcis-27234	128	40	.	.	PUNCT
fcis-27234	129	1	the	the	DET
fcis-27234	129	2	prediction	prediction	NOUN
fcis-27234	129	3	results	result	NOUN
fcis-27234	129	4	show	show	VERB
fcis-27234	129	5	that	that	SCONJ
fcis-27234	129	6	the	the	DET
fcis-27234	129	7	model	model	NOUN
fcis-27234	129	8	can	can	AUX
fcis-27234	129	9	accurately	accurately	ADV
fcis-27234	129	10	predict	predict	VERB
fcis-27234	129	11	the	the	DET
fcis-27234	129	12	start	start	NOUN
fcis-27234	129	13	and	and	CCONJ
fcis-27234	129	14	end	end	VERB
fcis-27234	129	15	time	time	NOUN
fcis-27234	129	16	of	of	ADP
fcis-27234	129	17	the	the	DET
fcis-27234	129	18	solar	solar	ADJ
fcis-27234	129	19	cycle	cycle	NOUN
fcis-27234	129	20	,	,	PUNCT
fcis-27234	129	21	the	the	DET
fcis-27234	129	22	start	start	ADJ
fcis-27234	129	23	time	time	NOUN
fcis-27234	129	24	and	and	CCONJ
fcis-27234	129	25	duration	duration	NOUN
fcis-27234	129	26	of	of	ADP
fcis-27234	129	27	the	the	DET
fcis-27234	129	28	solar	solar	ADJ
fcis-27234	129	29	maximum	maximum	NOUN
fcis-27234	129	30	in	in	ADP
fcis-27234	129	31	the	the	DET
fcis-27234	129	32	solar	solar	ADJ
fcis-27234	129	33	cycle	cycle	NOUN
fcis-27234	129	34	,	,	PUNCT
fcis-27234	129	35	as	as	ADV
fcis-27234	129	36	well	well	ADV
fcis-27234	129	37	as	as	ADP
fcis-27234	129	38	the	the	DET
fcis-27234	129	39	number	number	NOUN
fcis-27234	129	40	and	and	CCONJ
fcis-27234	129	41	area	area	NOUN
fcis-27234	129	42	of	of	ADP
fcis-27234	129	43	sunspots	sunspot	NOUN
fcis-27234	129	44	.	.	PUNCT
fcis-27234	130	1	in	in	ADP
fcis-27234	130	2	addition	addition	NOUN
fcis-27234	130	3	,	,	PUNCT
fcis-27234	130	4	the	the	DET
fcis-27234	130	5	reliability	reliability	NOUN
fcis-27234	130	6	of	of	ADP
fcis-27234	130	7	the	the	DET
fcis-27234	130	8	time	time	NOUN
fcis-27234	130	9	series	series	PROPN
fcis-27234	130	10	prediction	prediction	NOUN
fcis-27234	130	11	model	model	NOUN
fcis-27234	130	12	based	base	VERB
fcis-27234	130	13	on	on	ADP
fcis-27234	130	14	particle	particle	NOUN
fcis-27234	130	15	swarm	swarm	NOUN
fcis-27234	130	16	optimization	optimization	NOUN
fcis-27234	130	17	bp	bp	PROPN
fcis-27234	130	18	neural	neural	ADJ
fcis-27234	130	19	network	network	NOUN
fcis-27234	130	20	is	be	AUX
fcis-27234	130	21	verified	verify	VERB
fcis-27234	130	22	by	by	ADP
fcis-27234	130	23	comparing	compare	VERB
fcis-27234	130	24	it	it	PRON
fcis-27234	130	25	with	with	ADP
fcis-27234	130	26	bp	bp	PROPN
fcis-27234	130	27	neural	neural	PROPN
fcis-27234	130	28	network	network	PROPN
fcis-27234	130	29	model	model	NOUN
fcis-27234	130	30	.	.	PUNCT
fcis-27234	131	1	this	this	DET
fcis-27234	131	2	study	study	NOUN
fcis-27234	131	3	not	not	PART
fcis-27234	131	4	only	only	ADV
fcis-27234	131	5	provides	provide	VERB
fcis-27234	131	6	a	a	DET
fcis-27234	131	7	scientific	scientific	ADJ
fcis-27234	131	8	basis	basis	NOUN
fcis-27234	131	9	for	for	ADP
fcis-27234	131	10	the	the	DET
fcis-27234	131	11	prediction	prediction	NOUN
fcis-27234	131	12	of	of	ADP
fcis-27234	131	13	sunspot	sunspot	NOUN
fcis-27234	131	14	activity	activity	NOUN
fcis-27234	131	15	,	,	PUNCT
fcis-27234	131	16	but	but	CCONJ
fcis-27234	131	17	also	also	ADV
fcis-27234	131	18	provides	provide	VERB
fcis-27234	131	19	a	a	DET
fcis-27234	131	20	reference	reference	NOUN
fcis-27234	131	21	for	for	ADP
fcis-27234	131	22	the	the	DET
fcis-27234	131	23	prediction	prediction	NOUN
fcis-27234	131	24	of	of	ADP
fcis-27234	131	25	other	other	ADJ
fcis-27234	131	26	data	datum	NOUN
fcis-27234	131	27	with	with	ADP
fcis-27234	131	28	periodic	periodic	ADJ
fcis-27234	131	29	changes	change	NOUN
fcis-27234	131	30	.	.	PUNCT
fcis-27234	132	1	references	reference	NOUN
fcis-27234	132	2	[	[	X
fcis-27234	132	3	1	1	NUM
fcis-27234	132	4	]	]	X
fcis-27234	132	5	likejun	likejun	PROPN
fcis-27234	132	6	sutongwei	sutongwei	PROPN
fcis-27234	132	7	lianghongfei.periodicity	lianghongfei.periodicity	NOUN
fcis-27234	132	8	of	of	ADP
fcis-27234	132	9	sunspot	sunspot	NOUN
fcis-27234	132	10	activity	activity	NOUN
fcis-27234	132	11	in	in	ADP
fcis-27234	132	12	the	the	DET
fcis-27234	132	13	modern	modern	ADJ
fcis-27234	132	14	solar	solar	ADJ
fcis-27234	132	15	cycles[j	cycles[j	PROPN
fcis-27234	132	16	]	]	PUNCT
fcis-27234	132	17	.	.	PUNCT
fcis-27234	133	1	chinese	chinese	ADJ
fcis-27234	133	2	science	science	PROPN
fcis-27234	133	3	bulletin	bulletin	NOUN
fcis-27234	133	4	,	,	PUNCT
fcis-27234	133	5	2004	2004	NUM
fcis-27234	133	6	,	,	PUNCT
fcis-27234	133	7	49	49	NUM
fcis-27234	133	8	(	(	PUNCT
fcis-27234	133	9	21	21	NUM
fcis-27234	133	10	)	)	PUNCT
fcis-27234	133	11	:	:	PUNCT
fcis-27234	133	12	2247	2247	NUM
fcis-27234	133	13	-	-	SYM
fcis-27234	133	14	2252	2252	NUM
fcis-27234	133	15	.	.	PUNCT
fcis-27234	134	1	[	[	X
fcis-27234	134	2	2	2	NUM
fcis-27234	134	3	]	]	PUNCT
fcis-27234	134	4	abdisa	abdisa	NOUN
fcis-27234	134	5	g.	g.	PROPN
fcis-27234	134	6	dufera	dufera	PROPN
fcis-27234	134	7	,	,	PUNCT
fcis-27234	134	8	tiantian	tiantian	PROPN
fcis-27234	134	9	liu	liu	PROPN
fcis-27234	134	10	,	,	PUNCT
fcis-27234	134	11	jin	jin	NOUN
fcis-27234	134	12	xu.regression	xu.regression	NOUN
fcis-27234	134	13	models	model	NOUN
fcis-27234	134	14	of	of	ADP
fcis-27234	134	15	pearson	pearson	PROPN
fcis-27234	134	16	correlation	correlation	PROPN
fcis-27234	134	17	coefficient[j].statistical	coefficient[j].statistical	ADJ
fcis-27234	134	18	theory	theory	NOUN
fcis-27234	134	19	and	and	CCONJ
fcis-27234	134	20	the	the	DET
fcis-27234	134	21	related	related	ADJ
fcis-27234	134	22	fields	field	NOUN
fcis-27234	134	23	,	,	PUNCT
fcis-27234	134	24	2023	2023	NUM
fcis-27234	134	25	,	,	PUNCT
fcis-27234	134	26	7	7	NUM
fcis-27234	134	27	(	(	PUNCT
fcis-27234	134	28	02	02	NUM
fcis-27234	134	29	)	)	PUNCT
fcis-27234	134	30	:	:	PUNCT
fcis-27234	135	1	97	97	NUM
fcis-27234	135	2	-	-	SYM
fcis-27234	135	3	106	106	NUM
fcis-27234	135	4	.	.	PUNCT
fcis-27234	136	1	the	the	DET
fcis-27234	136	2	doi	doi	NOUN
fcis-27234	136	3	:	:	PUNCT
fcis-27234	136	4	10.1080/	10.1080/	NUM
fcis-27234	136	5	2475	2475	NUM
fcis-27234	136	6	4269	4269	NUM
fcis-27234	136	7	.	.	PUNCT
fcis-27234	137	1	2023.2164970	2023.2164970	PROPN
fcis-27234	137	2	.	.	PUNCT
fcis-27234	138	1	[	[	X
fcis-27234	138	2	3	3	X
fcis-27234	138	3	]	]	X
fcis-27234	138	4	daniel	daniel	PROPN
fcis-27234	138	5	okoh	okoh	PROPN
fcis-27234	138	6	,	,	PUNCT
fcis-27234	138	7	loretta	loretta	PROPN
fcis-27234	138	8	onuorah	onuorah	PROPN
fcis-27234	138	9	,	,	PUNCT
fcis-27234	138	10	babatunde	babatunde	NOUN
fcis-27234	138	11	rabiu	rabiu	NOUN
fcis-27234	138	12	,	,	PUNCT
fcis-27234	138	13	et	et	NOUN
fcis-27234	138	14	al.an	al.an	PROPN
fcis-27234	138	15	application	application	NOUN
fcis-27234	138	16	of	of	ADP
fcis-27234	138	17	artificial	artificial	ADJ
fcis-27234	138	18	intelligence	intelligence	NOUN
fcis-27234	138	19	for	for	ADP
fcis-27234	138	20	investigating	investigate	VERB
fcis-27234	138	21	the	the	DET
fcis-27234	138	22	effect	effect	NOUN
fcis-27234	138	23	of	of	ADP
fcis-27234	138	24	covid-19	covid-19	PROPN
fcis-27234	138	25	lockdown	lockdown	NOUN
fcis-27234	138	26	on	on	ADP
fcis-27234	138	27	three	three	NUM
fcis-27234	138	28	-	-	PUNCT
fcis-27234	138	29	dimensional	dimensional	ADJ
fcis-27234	138	30	temperature	temperature	NOUN
fcis-27234	138	31	variation	variation	NOUN
fcis-27234	138	32	in	in	ADP
fcis-27234	138	33	equatorial	equatorial	PROPN
fcis-27234	138	34	africa	africa	PROPN
fcis-27234	139	1	[	[	X
fcis-27234	139	2	j	j	X
fcis-27234	139	3	]	]	X
fcis-27234	139	4	.	.	PUNCT
fcis-27234	140	1	j	j	PROPN
fcis-27234	140	2	team	team	NOUN
fcis-27234	140	3	frontiers	frontier	NOUN
fcis-27234	140	4	,	,	PUNCT
fcis-27234	140	5	2022	2022	NUM
fcis-27234	140	6	,	,	PUNCT
fcis-27234	140	7	13	13	NUM
fcis-27234	140	8	(	(	PUNCT
fcis-27234	140	9	02	02	NUM
fcis-27234	140	10	):	):	PUNCT
fcis-27234	140	11	52	52	NUM
fcis-27234	140	12	-	-	SYM
fcis-27234	140	13	61	61	NUM
fcis-27234	140	14	.	.	PUNCT
fcis-27234	141	1	doi	doi	NOUN
fcis-27234	141	2	:	:	PUNCT
fcis-27234	141	3	10.1016	10.1016	NUM
fcis-27234	141	4	/	/	SYM
fcis-27234	141	5	j.g	j.g	PROPN
fcis-27234	141	6	sf	sf	PROPN
fcis-27234	141	7	.	.	PUNCT
fcis-27234	142	1	2021.101318	2021.101318	X
fcis-27234	142	2	.	.	PUNCT
fcis-27234	143	1	[	[	X
fcis-27234	143	2	4	4	NUM
fcis-27234	143	3	]	]	X
fcis-27234	143	4	qiang	qiang	PROPN
fcis-27234	143	5	li	li	PROPN
fcis-27234	143	6	,	,	PUNCT
fcis-27234	143	7	miao	miao	PROPN
fcis-27234	143	8	wan	wan	PROPN
fcis-27234	143	9	,	,	PUNCT
fcis-27234	143	10	shuguang	shuguang	PROPN
fcis-27234	143	11	zeng	zeng	PROPN
fcis-27234	143	12	,	,	PUNCT
fcis-27234	143	13	et	et	PROPN
fcis-27234	143	14	al.predicting	al.predicte	VERB
fcis-27234	143	15	the	the	DET
fcis-27234	143	16	25th	25th	ADJ
fcis-27234	143	17	solar	solar	ADJ
fcis-27234	143	18	cycle	cycle	NOUN
fcis-27234	143	19	using	use	VERB
fcis-27234	143	20	deep	deep	ADJ
fcis-27234	143	21	learning	learning	NOUN
fcis-27234	143	22	methods	method	NOUN
fcis-27234	143	23	based	base	VERB
fcis-27234	143	24	on	on	ADP
fcis-27234	143	25	sunspot	sunspot	NOUN
fcis-27234	143	26	area	area	NOUN
fcis-27234	143	27	data[j].research	data[j].research	VERB
fcis-27234	143	28	in	in	ADP
fcis-27234	143	29	astronomy	astronomy	NOUN
fcis-27234	143	30	and	and	CCONJ
fcis-27234	143	31	astrophysics	astrophysic	NOUN
fcis-27234	143	32	,	,	PUNCT
fcis-27234	143	33	2021,21	2021,21	NUM
fcis-27234	143	34	(	(	PUNCT
fcis-27234	143	35	07):290	07):290	NUM
fcis-27234	143	36	-	-	PUNCT
fcis-27234	143	37	298	298	NUM
fcis-27234	143	38	.	.	PUNCT
fcis-27234	144	1	(	(	PUNCT
fcis-27234	144	2	in	in	ADP
fcis-27234	144	3	chinese	chinese	ADJ
fcis-27234	144	4	)	)	PUNCT
fcis-27234	144	5	doi:10.1088/1674	doi:10.1088/1674	NOUN
fcis-27234	144	6	-	-	PUNCT
fcis-27234	144	7	4527/21/7/184	4527/21/7/184	NOUN
fcis-27234	144	8	.	.	PUNCT
fcis-27234	145	1	[	[	X
fcis-27234	145	2	5	5	X
fcis-27234	145	3	]	]	X
fcis-27234	145	4	liu	liu	PROPN
fcis-27234	145	5	shijun	shijun	PROPN
fcis-27234	145	6	,	,	PUNCT
fcis-27234	145	7	yu	yu	PROPN
fcis-27234	145	8	xiaoding	xiaoding	PROPN
fcis-27234	145	9	&	&	CCONJ
fcis-27234	145	10	amp	amp	PROPN
fcis-27234	145	11	;	;	PUNCT
fcis-27234	145	12	chen	chen	PROPN
fcis-27234	145	13	yongyi	yongyi	PROPN
fcis-27234	145	14	chinese	chinese	PROPN
fcis-27234	145	15	meteorological	meteorological	ADJ
fcis-27234	145	16	administration	administration	PROPN
fcis-27234	145	17	training	training	NOUN
fcis-27234	145	18	centre	centre	NOUN
fcis-27234	145	19	,	,	PUNCT
fcis-27234	145	20	beijing	beijing	PROPN
fcis-27234	145	21	,	,	PUNCT
fcis-27234	145	22	china	china	PROPN
fcis-27234	145	23	correspondence	correspondence	NOUN
fcis-27234	145	24	should	should	AUX
fcis-27234	145	25	be	be	AUX
fcis-27234	145	26	addressed	address	VERB
fcis-27234	145	27	to	to	ADP
fcis-27234	145	28	liu	liu	PROPN
fcis-27234	145	29	shijun.prediction	shijun.prediction	PROPN
fcis-27234	145	30	of	of	ADP
fcis-27234	145	31	solar	solar	ADJ
fcis-27234	145	32	cycle	cycle	NOUN
fcis-27234	145	33	based	base	VERB
fcis-27234	145	34	on	on	ADP
fcis-27234	145	35	the	the	DET
fcis-27234	145	36	invariant	invariant	ADJ
fcis-27234	145	37	[	[	X
fcis-27234	145	38	j].chinese	j].chinese	ADJ
fcis-27234	145	39	science	science	NOUN
fcis-27234	145	40	bulletin,2003,48(23):2568	bulletin,2003,48(23):2568	PROPN
fcis-27234	145	41	-	-	PUNCT
fcis-27234	145	42	2571	2571	NUM
fcis-27234	145	43	.	.	PUNCT
fcis-27234	146	1	[	[	X
fcis-27234	146	2	6	6	NUM
fcis-27234	146	3	]	]	X
fcis-27234	146	4	yibin	yibin	PROPN
fcis-27234	146	5	yao	yao	PROPN
fcis-27234	146	6	,	,	PUNCT
fcis-27234	146	7	bao	bao	PROPN
fcis-27234	146	8	zhang	zhang	PROPN
fcis-27234	146	9	,	,	PUNCT
fcis-27234	146	10	chaoqian	chaoqian	PROPN
fcis-27234	146	11	xu	xu	PROPN
fcis-27234	146	12	,	,	PUNCT
fcis-27234	146	13	et	et	NOUN
fcis-27234	146	14	al.analysis	al.analysis	NOUN
fcis-27234	146	15	of	of	ADP
fcis-27234	146	16	the	the	DET
fcis-27234	146	17	global	global	ADJ
fcis-27234	146	18	t_m	t_m	NUM
fcis-27234	146	19	–	–	PUNCT
fcis-27234	146	20	t_s	t_s	SYM
fcis-27234	146	21	correlation	correlation	NOUN
fcis-27234	146	22	and	and	CCONJ
fcis-27234	146	23	establishment	establishment	NOUN
fcis-27234	146	24	of	of	ADP
fcis-27234	146	25	the	the	DET
fcis-27234	146	26	latituderelated	latituderelate	VERB
fcis-27234	146	27	linear	linear	PROPN
fcis-27234	146	28	model[j].chinese	model[j].chinese	PROPN
fcis-27234	146	29	science	science	PROPN
fcis-27234	146	30	bulletin,2014	bulletin,2014	PROPN
fcis-27234	146	31	,	,	PUNCT
fcis-27234	146	32	59(19	59(19	NUM
fcis-27234	146	33	):	):	PUNCT
fcis-27234	146	34	2340	2340	NUM
fcis-27234	146	35	-	-	SYM
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fcis-27234	146	37	.	.	PUNCT
fcis-27234	147	1	[	[	X
fcis-27234	147	2	7	7	X
fcis-27234	147	3	]	]	X
fcis-27234	147	4	wei	wei	PROPN
fcis-27234	147	5	qian	qian	PROPN
fcis-27234	147	6	,	,	PUNCT
fcis-27234	147	7	yanmin	yanmin	PROPN
fcis-27234	147	8	wu	wu	PROPN
fcis-27234	147	9	,	,	PUNCT
fcis-27234	147	10	bo	bo	PROPN
fcis-27234	147	11	shen.novel	shen.novel	NOUN
fcis-27234	147	12	adaptive	adaptive	ADJ
fcis-27234	147	13	memory	memory	NOUN
fcis-27234	147	14	event	event	NOUN
fcis-27234	147	15	-	-	PUNCT
fcis-27234	147	16	triggered	trigger	VERB
fcis-27234	147	17	-	-	PUNCT
fcis-27234	147	18	based	base	VERB
fcis-27234	147	19	fuzzy	fuzzy	ADJ
fcis-27234	147	20	robust	robust	ADJ
fcis-27234	147	21	control	control	NOUN
fcis-27234	147	22	for	for	ADP
fcis-27234	147	23	nonlinear	nonlinear	ADJ
fcis-27234	147	24	networked	networked	ADJ
fcis-27234	147	25	systems	system	NOUN
fcis-27234	147	26	via	via	ADP
fcis-27234	147	27	the	the	DET
fcis-27234	147	28	differential	differential	ADJ
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fcis-27234	147	31	[	[	X
fcis-27234	147	32	j	j	X
fcis-27234	147	33	]	]	X
fcis-27234	147	34	.	.	PUNCT
fcis-27234	147	35	ieee	ieee	PROPN
fcis-27234	147	36	/	/	SYM
fcis-27234	147	37	caa	caa	PROPN
fcis-27234	147	38	journal	journal	PROPN
fcis-27234	147	39	of	of	ADP
fcis-27234	147	40	automatica	automatica	PROPN
fcis-27234	147	41	sinica	sinica	PROPN
fcis-27234	147	42	,	,	PUNCT
fcis-27234	147	43	2024	2024	NUM
fcis-27234	147	44	,	,	PUNCT
fcis-27234	147	45	11	11	NUM
fcis-27234	147	46	(	(	PUNCT
fcis-27234	147	47	8)	8)	NUM
fcis-27234	147	48	:	:	SYM
fcis-27234	147	49	1836	1836	NUM
fcis-27234	147	50	-	-	SYM
fcis-27234	147	51	1848	1848	NUM
fcis-27234	147	52	.	.	PUNCT
fcis-27234	148	1	the	the	DET
fcis-27234	148	2	doi	doi	NOUN
fcis-27234	148	3	:	:	PUNCT
fcis-27234	148	4	10.1109	10.1109	NUM
fcis-27234	148	5	/	/	SYM
fcis-27234	148	6	jas	jas	PROPN
fcis-27234	148	7	.	.	PUNCT
fcis-27234	149	1	2024.124419	2024.124419	X
fcis-27234	149	2	.	.	PUNCT
fcis-27234	150	1	[	[	X
fcis-27234	150	2	8	8	NUM
fcis-27234	150	3	]	]	SYM
fcis-27234	150	4	tang	tang	PROPN
fcis-27234	150	5	,	,	PUNCT
fcis-27234	150	6	lin	lin	PROPN
fcis-27234	150	7	,	,	PUNCT
fcis-27234	150	8	xu	xu	PROPN
fcis-27234	150	9	,	,	PUNCT
fcis-27234	150	10	zhi	zhi	PROPN
fcis-27234	150	11	-	-	PUNCT
fcis-27234	150	12	pei	pei	PROPN
fcis-27234	150	13	,	,	PUNCT
fcis-27234	150	14	he	he	PRON
fcis-27234	150	15	,	,	PUNCT
fcis-27234	150	16	tian	tian	PROPN
fcis-27234	150	17	-	-	PUNCT
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fcis-27234	150	29	bp	bp	PROPN
fcis-27234	150	30	neural	neural	PROPN
fcis-27234	150	31	network[j].chinese	network[j].chinese	PROPN
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fcis-27234	150	33	of	of	ADP
fcis-27234	150	34	engineering	engineer	VERB
fcis-27234	150	35	the	the	DET
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fcis-27234	150	37	,	,	PUNCT
fcis-27234	150	38	2018	2018	NUM
fcis-27234	150	39	,	,	PUNCT
fcis-27234	150	40	25	25	NUM
fcis-27234	150	41	(	(	PUNCT
fcis-27234	150	42	5	5	NUM
fcis-27234	150	43	)	)	PUNCT
fcis-27234	150	44	:	:	PUNCT
fcis-27234	150	45	576	576	NUM
fcis-27234	150	46	-	-	SYM
fcis-27234	150	47	582	582	NUM
fcis-27234	150	48	.	.	PUNCT
fcis-27234	151	1	the	the	DET
fcis-27234	151	2	doi	doi	NOUN
fcis-27234	151	3	:	:	PUNCT
fcis-27234	151	4	10.3785	10.3785	NUM
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fcis-27234	151	7	ssn	ssn	PROPN
fcis-27234	151	8	.	.	PROPN
fcis-27234	151	9	1006	1006	NUM
fcis-27234	151	10	-	-	SYM
fcis-27234	151	11	754	754	NUM
fcis-27234	151	12	x.	x.	NOUN
fcis-27234	151	13	2018.05.012	2018.05.012	NOUN
fcis-27234	151	14	.	.	PUNCT