id	sid	tid	token	lemma	pos
fcis-17185	1	1	frontiers	frontier	NOUN
fcis-17185	1	2	in	in	ADP
fcis-17185	1	3	computing	computing	NOUN
fcis-17185	1	4	and	and	CCONJ
fcis-17185	1	5	intelligent	intelligent	ADJ
fcis-17185	1	6	systems	system	NOUN
fcis-17185	1	7	issn	issn	VERB
fcis-17185	1	8	:	:	PUNCT
fcis-17185	1	9	2832	2832	NUM
fcis-17185	1	10	-	-	SYM
fcis-17185	1	11	6024	6024	NUM
fcis-17185	1	12	|	|	NOUN
fcis-17185	1	13	vol	vol	NOUN
fcis-17185	1	14	.	.	PROPN
fcis-17185	2	1	7	7	NUM
fcis-17185	2	2	,	,	PUNCT
fcis-17185	2	3	no	no	INTJ
fcis-17185	2	4	.	.	NOUN
fcis-17185	2	5	1	1	NUM
fcis-17185	2	6	,	,	PUNCT
fcis-17185	2	7	2024	2024	NUM
fcis-17185	2	8	64	64	NUM
fcis-17185	2	9	extreme	extreme	ADJ
fcis-17185	2	10	learning	learning	NOUN
fcis-17185	2	11	machine	machine	NOUN
fcis-17185	2	12	classification	classification	NOUN
fcis-17185	2	13	method	method	NOUN
fcis-17185	2	14	based	base	VERB
fcis-17185	2	15	on	on	ADP
fcis-17185	2	16	twin	twin	ADJ
fcis-17185	2	17	strategy	strategy	NOUN
fcis-17185	2	18	salp	salp	NOUN
fcis-17185	2	19	swarm	swarm	NOUN
fcis-17185	2	20	algorithm	algorithm	PROPN
fcis-17185	2	21	meiling	meile	VERB
fcis-17185	2	22	shang	shang	PROPN
fcis-17185	2	23	*	*	PROPN
fcis-17185	2	24	,	,	PUNCT
fcis-17185	2	25	rongguo	rongguo	PROPN
fcis-17185	2	26	qu	qu	PROPN
fcis-17185	2	27	,	,	PUNCT
fcis-17185	2	28	deqing	deqing	PROPN
fcis-17185	2	29	ji	ji	PROPN
fcis-17185	2	30	,	,	PUNCT
fcis-17185	2	31	zhenxing	zhenxing	PROPN
fcis-17185	2	32	yu	yu	PROPN
fcis-17185	2	33	,	,	PUNCT
fcis-17185	2	34	qinwei	qinwei	VERB
fcis-17185	2	35	fan	fan	PROPN
fcis-17185	2	36	xi’an	xi’an	PROPN
fcis-17185	2	37	polytechnic	polytechnic	PROPN
fcis-17185	2	38	university	university	PROPN
fcis-17185	2	39	,	,	PUNCT
fcis-17185	2	40	xi’an	xi’an	PROPN
fcis-17185	2	41	shaanxi	shaanxi	PROPN
fcis-17185	2	42	,	,	PUNCT
fcis-17185	2	43	710600	710600	NUM
fcis-17185	2	44	,	,	PUNCT
fcis-17185	2	45	china	china	PROPN
fcis-17185	2	46	*	*	PUNCT
fcis-17185	2	47	corresponding	correspond	VERB
fcis-17185	2	48	author	author	NOUN
fcis-17185	2	49	:	:	PUNCT
fcis-17185	2	50	meiling	meile	VERB
fcis-17185	2	51	shang	shang	PROPN
fcis-17185	2	52	abstract	abstract	NOUN
fcis-17185	2	53	:	:	PUNCT
fcis-17185	2	54	extreme	extreme	ADJ
fcis-17185	2	55	learning	learning	NOUN
fcis-17185	2	56	machines	machine	NOUN
fcis-17185	2	57	(	(	PUNCT
fcis-17185	2	58	elm	elm	PROPN
fcis-17185	2	59	)	)	PUNCT
fcis-17185	2	60	are	be	AUX
fcis-17185	2	61	a	a	DET
fcis-17185	2	62	type	type	NOUN
fcis-17185	2	63	of	of	ADP
fcis-17185	2	64	single	single	ADJ
fcis-17185	2	65	hidden	hide	VERB
fcis-17185	2	66	layer	layer	NOUN
fcis-17185	2	67	feedforward	feedforward	NOUN
fcis-17185	2	68	neural	neural	ADJ
fcis-17185	2	69	network	network	NOUN
fcis-17185	2	70	(	(	PUNCT
fcis-17185	2	71	slfn	slfn	NOUN
fcis-17185	2	72	)	)	PUNCT
fcis-17185	2	73	.	.	PUNCT
fcis-17185	3	1	in	in	ADP
fcis-17185	3	2	recent	recent	ADJ
fcis-17185	3	3	years	year	NOUN
fcis-17185	3	4	,	,	PUNCT
fcis-17185	3	5	they	they	PRON
fcis-17185	3	6	have	have	AUX
fcis-17185	3	7	attracted	attract	VERB
fcis-17185	3	8	attention	attention	NOUN
fcis-17185	3	9	for	for	ADP
fcis-17185	3	10	their	their	PRON
fcis-17185	3	11	powerful	powerful	ADJ
fcis-17185	3	12	approximation	approximation	NOUN
fcis-17185	3	13	ability	ability	NOUN
fcis-17185	3	14	and	and	CCONJ
fcis-17185	3	15	fast	fast	ADJ
fcis-17185	3	16	learning	learning	NOUN
fcis-17185	3	17	speed	speed	NOUN
fcis-17185	3	18	.	.	PUNCT
fcis-17185	4	1	compared	compare	VERB
fcis-17185	4	2	with	with	ADP
fcis-17185	4	3	traditional	traditional	ADJ
fcis-17185	4	4	neural	neural	ADJ
fcis-17185	4	5	network	network	NOUN
fcis-17185	4	6	algorithms	algorithm	NOUN
fcis-17185	4	7	,	,	PUNCT
fcis-17185	4	8	elm	elm	PROPN
fcis-17185	4	9	have	have	VERB
fcis-17185	4	10	the	the	DET
fcis-17185	4	11	advantages	advantage	NOUN
fcis-17185	4	12	of	of	ADP
fcis-17185	4	13	simple	simple	ADJ
fcis-17185	4	14	structure	structure	NOUN
fcis-17185	4	15	,	,	PUNCT
fcis-17185	4	16	fast	fast	ADJ
fcis-17185	4	17	learning	learning	NOUN
fcis-17185	4	18	speed	speed	NOUN
fcis-17185	4	19	,	,	PUNCT
fcis-17185	4	20	and	and	CCONJ
fcis-17185	4	21	good	good	ADJ
fcis-17185	4	22	generalization	generalization	NOUN
fcis-17185	4	23	performance	performance	NOUN
fcis-17185	4	24	.	.	PUNCT
fcis-17185	5	1	however	however	ADV
fcis-17185	5	2	,	,	PUNCT
fcis-17185	5	3	since	since	SCONJ
fcis-17185	5	4	the	the	DET
fcis-17185	5	5	input	input	NOUN
fcis-17185	5	6	weights	weight	NOUN
fcis-17185	5	7	and	and	CCONJ
fcis-17185	5	8	biases	bias	NOUN
fcis-17185	5	9	of	of	ADP
fcis-17185	5	10	elm	elm	NOUN
fcis-17185	5	11	are	be	AUX
fcis-17185	5	12	randomly	randomly	ADV
fcis-17185	5	13	generated	generate	VERB
fcis-17185	5	14	,	,	PUNCT
fcis-17185	5	15	there	there	PRON
fcis-17185	5	16	may	may	AUX
fcis-17185	5	17	be	be	AUX
fcis-17185	5	18	some	some	DET
fcis-17185	5	19	suboptimal	suboptimal	ADJ
fcis-17185	5	20	or	or	CCONJ
fcis-17185	5	21	unnecessary	unnecessary	ADJ
fcis-17185	5	22	input	input	NOUN
fcis-17185	5	23	weights	weight	NOUN
fcis-17185	5	24	and	and	CCONJ
fcis-17185	5	25	biases	bias	NOUN
fcis-17185	5	26	.	.	PUNCT
fcis-17185	6	1	in	in	ADP
fcis-17185	6	2	addition	addition	NOUN
fcis-17185	6	3	,	,	PUNCT
fcis-17185	6	4	elm	elm	NOUN
fcis-17185	6	5	may	may	AUX
fcis-17185	6	6	require	require	VERB
fcis-17185	6	7	more	more	ADV
fcis-17185	6	8	hidden	hidden	ADJ
fcis-17185	6	9	nodes	node	NOUN
fcis-17185	6	10	,	,	PUNCT
fcis-17185	6	11	which	which	PRON
fcis-17185	6	12	may	may	AUX
fcis-17185	6	13	slow	slow	VERB
fcis-17185	6	14	down	down	ADP
fcis-17185	6	15	its	its	PRON
fcis-17185	6	16	response	response	NOUN
fcis-17185	6	17	to	to	ADP
fcis-17185	6	18	unknown	unknown	ADJ
fcis-17185	6	19	test	test	NOUN
fcis-17185	6	20	data	datum	NOUN
fcis-17185	6	21	.	.	PUNCT
fcis-17185	7	1	to	to	PART
fcis-17185	7	2	address	address	VERB
fcis-17185	7	3	these	these	DET
fcis-17185	7	4	issues	issue	NOUN
fcis-17185	7	5	,	,	PUNCT
fcis-17185	7	6	a	a	DET
fcis-17185	7	7	twin	twin	ADJ
fcis-17185	7	8	strategy	strategy	NOUN
fcis-17185	7	9	salp	salp	NOUN
fcis-17185	7	10	swarm	swarm	NOUN
fcis-17185	7	11	algorithm	algorithm	NOUN
fcis-17185	7	12	(	(	PUNCT
fcis-17185	7	13	tssa	tssa	NOUN
fcis-17185	7	14	)	)	PUNCT
fcis-17185	7	15	is	be	AUX
fcis-17185	7	16	proposed	propose	VERB
fcis-17185	7	17	to	to	PART
fcis-17185	7	18	increase	increase	VERB
fcis-17185	7	19	the	the	DET
fcis-17185	7	20	population	population	NOUN
fcis-17185	7	21	diversity	diversity	NOUN
fcis-17185	7	22	to	to	PART
fcis-17185	7	23	improve	improve	VERB
fcis-17185	7	24	the	the	DET
fcis-17185	7	25	convergence	convergence	NOUN
fcis-17185	7	26	speed	speed	NOUN
fcis-17185	7	27	of	of	ADP
fcis-17185	7	28	the	the	DET
fcis-17185	7	29	algorithm	algorithm	NOUN
fcis-17185	7	30	through	through	ADP
fcis-17185	7	31	chaotic	chaotic	ADJ
fcis-17185	7	32	initialization	initialization	NOUN
fcis-17185	7	33	,	,	PUNCT
fcis-17185	7	34	while	while	SCONJ
fcis-17185	7	35	shock	shock	NOUN
fcis-17185	7	36	inertia	inertia	NOUN
fcis-17185	7	37	weights	weight	NOUN
fcis-17185	7	38	and	and	CCONJ
fcis-17185	7	39	learning	learn	VERB
fcis-17185	7	40	paradigms	paradigm	NOUN
fcis-17185	7	41	are	be	AUX
fcis-17185	7	42	introduced	introduce	VERB
fcis-17185	7	43	into	into	ADP
fcis-17185	7	44	the	the	DET
fcis-17185	7	45	follower	follower	NOUN
fcis-17185	7	46	position	position	NOUN
fcis-17185	7	47	updating	update	VERB
fcis-17185	7	48	to	to	PART
fcis-17185	7	49	enhance	enhance	VERB
fcis-17185	7	50	the	the	DET
fcis-17185	7	51	stochasticity	stochasticity	NOUN
fcis-17185	7	52	of	of	ADP
fcis-17185	7	53	the	the	DET
fcis-17185	7	54	particles	particle	NOUN
fcis-17185	7	55	.	.	PUNCT
fcis-17185	8	1	classification	classification	NOUN
fcis-17185	8	2	tests	test	NOUN
fcis-17185	8	3	are	be	AUX
fcis-17185	8	4	conducted	conduct	VERB
fcis-17185	8	5	on	on	ADP
fcis-17185	8	6	six	six	NUM
fcis-17185	8	7	datasets	dataset	NOUN
fcis-17185	8	8	using	use	VERB
fcis-17185	8	9	the	the	DET
fcis-17185	8	10	proposed	propose	VERB
fcis-17185	8	11	tssa	tssa	NOUN
fcis-17185	8	12	,	,	PUNCT
fcis-17185	8	13	and	and	CCONJ
fcis-17185	8	14	the	the	DET
fcis-17185	8	15	experimental	experimental	ADJ
fcis-17185	8	16	results	result	NOUN
fcis-17185	8	17	show	show	VERB
fcis-17185	8	18	that	that	SCONJ
fcis-17185	8	19	the	the	DET
fcis-17185	8	20	proposed	propose	VERB
fcis-17185	8	21	tssa	tssa	NOUN
fcis-17185	8	22	-	-	PUNCT
fcis-17185	8	23	elm	elm	NOUN
fcis-17185	8	24	has	have	VERB
fcis-17185	8	25	higher	high	ADJ
fcis-17185	8	26	accuracy	accuracy	NOUN
fcis-17185	8	27	and	and	CCONJ
fcis-17185	8	28	better	well	ADJ
fcis-17185	8	29	generalization	generalization	NOUN
fcis-17185	8	30	performance	performance	NOUN
fcis-17185	8	31	than	than	ADP
fcis-17185	8	32	some	some	DET
fcis-17185	8	33	existing	exist	VERB
fcis-17185	8	34	elm	elm	NOUN
fcis-17185	8	35	variants	variant	NOUN
fcis-17185	8	36	.	.	PUNCT
fcis-17185	9	1	keywords	keyword	NOUN
fcis-17185	9	2	:	:	PUNCT
fcis-17185	9	3	extreme	extreme	ADJ
fcis-17185	9	4	learning	learning	NOUN
fcis-17185	9	5	machine	machine	NOUN
fcis-17185	9	6	;	;	PUNCT
fcis-17185	9	7	salp	salp	NOUN
fcis-17185	9	8	swarm	swarm	NOUN
fcis-17185	9	9	algorithm	algorithm	NOUN
fcis-17185	9	10	;	;	PUNCT
fcis-17185	9	11	multi	multi	ADJ
fcis-17185	9	12	-	-	NOUN
fcis-17185	9	13	strategy	strategy	NOUN
fcis-17185	9	14	;	;	PUNCT
fcis-17185	9	15	inertia	inertia	NOUN
fcis-17185	9	16	weights	weight	NOUN
fcis-17185	9	17	;	;	PUNCT
fcis-17185	9	18	classification	classification	NOUN
fcis-17185	9	19	problems	problem	NOUN
fcis-17185	9	20	.	.	PUNCT
fcis-17185	10	1	1	1	X
fcis-17185	10	2	.	.	X
fcis-17185	10	3	introduction	introduction	NOUN
fcis-17185	10	4	huang	huang	PROPN
fcis-17185	10	5	et	et	PROPN
fcis-17185	10	6	al	al	PROPN
fcis-17185	10	7	.	.	PROPN
fcis-17185	10	8	proposed	propose	VERB
fcis-17185	10	9	a	a	DET
fcis-17185	10	10	non	non	ADJ
fcis-17185	10	11	-	-	ADJ
fcis-17185	10	12	iterative	iterative	ADJ
fcis-17185	10	13	learning	learn	VERB
fcis-17185	10	14	algorithm	algorithm	NOUN
fcis-17185	10	15	based	base	VERB
fcis-17185	10	16	on	on	ADP
fcis-17185	10	17	feedforward	feedforward	ADJ
fcis-17185	10	18	neural	neural	ADJ
fcis-17185	10	19	networks	network	NOUN
fcis-17185	10	20	called	call	VERB
fcis-17185	10	21	extreme	extreme	ADJ
fcis-17185	10	22	learning	learning	NOUN
fcis-17185	10	23	machine	machine	NOUN
fcis-17185	10	24	(	(	PUNCT
fcis-17185	10	25	elm	elm	NOUN
fcis-17185	10	26	)	)	PUNCT
fcis-17185	10	27	in	in	ADP
fcis-17185	10	28	2004	2004	NUM
fcis-17185	11	1	[	[	X
fcis-17185	11	2	1	1	NUM
fcis-17185	11	3	]	]	PUNCT
fcis-17185	11	4	.	.	PUNCT
fcis-17185	12	1	compared	compare	VERB
fcis-17185	12	2	to	to	ADP
fcis-17185	12	3	other	other	ADJ
fcis-17185	12	4	traditional	traditional	ADJ
fcis-17185	12	5	learning	learn	VERB
fcis-17185	12	6	algorithms	algorithm	NOUN
fcis-17185	12	7	such	such	ADJ
fcis-17185	12	8	as	as	ADP
fcis-17185	12	9	feedforward	feedforward	ADJ
fcis-17185	12	10	neural	neural	ADJ
fcis-17185	12	11	networks	network	NOUN
fcis-17185	12	12	(	(	PUNCT
fcis-17185	12	13	fnn	fnn	PROPN
fcis-17185	12	14	)	)	PUNCT
fcis-17185	12	15	and	and	CCONJ
fcis-17185	12	16	backpropagation	backpropagation	NOUN
fcis-17185	12	17	(	(	PUNCT
fcis-17185	12	18	bp	bp	PROPN
fcis-17185	12	19	)	)	PUNCT
fcis-17185	12	20	,	,	PUNCT
fcis-17185	12	21	elm	elm	NOUN
fcis-17185	12	22	has	have	VERB
fcis-17185	12	23	advantages	advantage	NOUN
fcis-17185	12	24	in	in	ADP
fcis-17185	12	25	terms	term	NOUN
fcis-17185	12	26	of	of	ADP
fcis-17185	12	27	good	good	ADJ
fcis-17185	12	28	global	global	ADJ
fcis-17185	12	29	generalization	generalization	NOUN
fcis-17185	12	30	,	,	PUNCT
fcis-17185	12	31	high	high	ADJ
fcis-17185	12	32	realtime	realtime	NOUN
fcis-17185	12	33	performance	performance	NOUN
fcis-17185	12	34	,	,	PUNCT
fcis-17185	12	35	and	and	CCONJ
fcis-17185	12	36	minimal	minimal	ADJ
fcis-17185	12	37	manual	manual	ADJ
fcis-17185	12	38	parameter	parameter	NOUN
fcis-17185	12	39	tuning	tune	VERB
fcis-17185	12	40	[	[	X
fcis-17185	12	41	23	23	NUM
fcis-17185	12	42	]	]	PUNCT
fcis-17185	12	43	.	.	PUNCT
fcis-17185	13	1	it	it	PRON
fcis-17185	13	2	has	have	AUX
fcis-17185	13	3	been	be	AUX
fcis-17185	13	4	widely	widely	ADV
fcis-17185	13	5	applied	apply	VERB
fcis-17185	13	6	in	in	ADP
fcis-17185	13	7	various	various	ADJ
fcis-17185	13	8	fields	field	NOUN
fcis-17185	13	9	,	,	PUNCT
fcis-17185	13	10	including	include	VERB
fcis-17185	13	11	cloud	cloud	NOUN
fcis-17185	13	12	computing	computing	NOUN
fcis-17185	13	13	[	[	X
fcis-17185	13	14	4	4	NUM
fcis-17185	13	15	]	]	PUNCT
fcis-17185	13	16	,	,	PUNCT
fcis-17185	13	17	data	data	NOUN
fcis-17185	13	18	visualization	visualization	NOUN
fcis-17185	13	19	[	[	X
fcis-17185	13	20	5	5	NUM
fcis-17185	13	21	]	]	PUNCT
fcis-17185	13	22	,	,	PUNCT
fcis-17185	13	23	and	and	CCONJ
fcis-17185	13	24	random	random	ADJ
fcis-17185	13	25	projection	projection	NOUN
fcis-17185	13	26	[	[	X
fcis-17185	13	27	6	6	NUM
fcis-17185	13	28	]	]	PUNCT
fcis-17185	13	29	.	.	PUNCT
fcis-17185	14	1	however	however	ADV
fcis-17185	14	2	,	,	PUNCT
fcis-17185	14	3	due	due	ADP
fcis-17185	14	4	to	to	ADP
fcis-17185	14	5	the	the	DET
fcis-17185	14	6	random	random	ADJ
fcis-17185	14	7	generation	generation	NOUN
fcis-17185	14	8	of	of	ADP
fcis-17185	14	9	input	input	NOUN
fcis-17185	14	10	layer	layer	NOUN
fcis-17185	14	11	weights	weight	NOUN
fcis-17185	14	12	and	and	CCONJ
fcis-17185	14	13	thresholds	threshold	NOUN
fcis-17185	14	14	in	in	ADP
fcis-17185	14	15	elm	elm	PROPN
fcis-17185	14	16	,	,	PUNCT
fcis-17185	14	17	it	it	PRON
fcis-17185	14	18	may	may	AUX
fcis-17185	14	19	result	result	VERB
fcis-17185	14	20	in	in	ADP
fcis-17185	14	21	unnecessary	unnecessary	ADJ
fcis-17185	14	22	or	or	CCONJ
fcis-17185	14	23	suboptimal	suboptimal	ADJ
fcis-17185	14	24	weights	weight	NOUN
fcis-17185	14	25	and	and	CCONJ
fcis-17185	14	26	thresholds	threshold	NOUN
fcis-17185	14	27	.	.	PUNCT
fcis-17185	15	1	furthermore	furthermore	ADV
fcis-17185	15	2	,	,	PUNCT
fcis-17185	15	3	when	when	SCONJ
fcis-17185	15	4	using	use	VERB
fcis-17185	15	5	elm	elm	NOUN
fcis-17185	15	6	for	for	ADP
fcis-17185	15	7	prediction	prediction	NOUN
fcis-17185	15	8	,	,	PUNCT
fcis-17185	15	9	it	it	PRON
fcis-17185	15	10	may	may	AUX
fcis-17185	15	11	require	require	VERB
fcis-17185	15	12	more	more	ADV
fcis-17185	15	13	hidden	hidden	ADJ
fcis-17185	15	14	nodes	node	NOUN
fcis-17185	15	15	,	,	PUNCT
fcis-17185	15	16	leading	lead	VERB
fcis-17185	15	17	to	to	ADP
fcis-17185	15	18	slow	slow	ADJ
fcis-17185	15	19	response	response	NOUN
fcis-17185	15	20	to	to	ADP
fcis-17185	15	21	unknown	unknown	ADJ
fcis-17185	15	22	test	test	NOUN
fcis-17185	15	23	data	datum	NOUN
fcis-17185	15	24	.	.	PUNCT
fcis-17185	16	1	the	the	DET
fcis-17185	16	2	accuracy	accuracy	NOUN
fcis-17185	16	3	of	of	ADP
fcis-17185	16	4	prediction	prediction	NOUN
fcis-17185	16	5	results	result	NOUN
fcis-17185	16	6	depends	depend	VERB
fcis-17185	16	7	directly	directly	ADV
fcis-17185	16	8	on	on	ADP
fcis-17185	16	9	the	the	DET
fcis-17185	16	10	input	input	NOUN
fcis-17185	16	11	weights	weight	NOUN
fcis-17185	16	12	and	and	CCONJ
fcis-17185	16	13	thresholds	threshold	NOUN
fcis-17185	16	14	of	of	ADP
fcis-17185	16	15	the	the	DET
fcis-17185	16	16	hidden	hide	VERB
fcis-17185	16	17	layer	layer	NOUN
fcis-17185	16	18	neurons	neuron	NOUN
fcis-17185	16	19	,	,	PUNCT
fcis-17185	16	20	and	and	CCONJ
fcis-17185	16	21	using	use	VERB
fcis-17185	16	22	appropriate	appropriate	ADJ
fcis-17185	16	23	input	input	NOUN
fcis-17185	16	24	weights	weight	NOUN
fcis-17185	16	25	and	and	CCONJ
fcis-17185	16	26	thresholds	threshold	NOUN
fcis-17185	16	27	can	can	AUX
fcis-17185	16	28	effectively	effectively	ADV
fcis-17185	16	29	improve	improve	VERB
fcis-17185	16	30	the	the	DET
fcis-17185	16	31	accuracy	accuracy	NOUN
fcis-17185	16	32	of	of	ADP
fcis-17185	16	33	prediction	prediction	NOUN
fcis-17185	16	34	.	.	PUNCT
fcis-17185	17	1	therefore	therefore	ADV
fcis-17185	17	2	,	,	PUNCT
fcis-17185	17	3	many	many	ADJ
fcis-17185	17	4	researchers	researcher	NOUN
fcis-17185	17	5	have	have	AUX
fcis-17185	17	6	employed	employ	VERB
fcis-17185	17	7	various	various	ADJ
fcis-17185	17	8	methods	method	NOUN
fcis-17185	17	9	to	to	PART
fcis-17185	17	10	adjust	adjust	VERB
fcis-17185	17	11	the	the	DET
fcis-17185	17	12	input	input	NOUN
fcis-17185	17	13	layer	layer	NOUN
fcis-17185	17	14	weights	weight	NOUN
fcis-17185	17	15	and	and	CCONJ
fcis-17185	17	16	hidden	hide	VERB
fcis-17185	17	17	layer	layer	NOUN
fcis-17185	17	18	thresholds	threshold	NOUN
fcis-17185	17	19	of	of	ADP
fcis-17185	17	20	elm	elm	PROPN
fcis-17185	17	21	.	.	PUNCT
fcis-17185	18	1	zhu	zhu	PROPN
fcis-17185	18	2	et	et	PROPN
fcis-17185	18	3	al	al	PROPN
fcis-17185	18	4	.	.	PUNCT
fcis-17185	19	1	[	[	X
fcis-17185	19	2	7	7	X
fcis-17185	19	3	]	]	PUNCT
fcis-17185	19	4	proposed	propose	VERB
fcis-17185	19	5	a	a	DET
fcis-17185	19	6	fusion	fusion	NOUN
fcis-17185	19	7	of	of	ADP
fcis-17185	19	8	the	the	DET
fcis-17185	19	9	differential	differential	ADJ
fcis-17185	19	10	evolution	evolution	NOUN
fcis-17185	19	11	algorithm	algorithm	NOUN
fcis-17185	19	12	to	to	PART
fcis-17185	19	13	optimize	optimize	VERB
fcis-17185	19	14	the	the	DET
fcis-17185	19	15	input	input	NOUN
fcis-17185	19	16	layer	layer	NOUN
fcis-17185	19	17	weights	weight	NOUN
fcis-17185	19	18	of	of	ADP
fcis-17185	19	19	elm	elm	PROPN
fcis-17185	19	20	.	.	PUNCT
fcis-17185	20	1	ling	ling	PROPN
fcis-17185	20	2	et	et	PROPN
fcis-17185	20	3	al	al	PROPN
fcis-17185	20	4	.	.	PUNCT
fcis-17185	21	1	[	[	X
fcis-17185	21	2	8	8	NUM
fcis-17185	21	3	]	]	PUNCT
fcis-17185	21	4	used	use	VERB
fcis-17185	21	5	the	the	DET
fcis-17185	21	6	particle	particle	NOUN
fcis-17185	21	7	swarm	swarm	NOUN
fcis-17185	21	8	optimization	optimization	NOUN
fcis-17185	21	9	algorithm	algorithm	NOUN
fcis-17185	21	10	to	to	PART
fcis-17185	21	11	optimize	optimize	VERB
fcis-17185	21	12	the	the	DET
fcis-17185	21	13	weights	weight	NOUN
fcis-17185	21	14	of	of	ADP
fcis-17185	21	15	the	the	DET
fcis-17185	21	16	input	input	NOUN
fcis-17185	21	17	layer	layer	NOUN
fcis-17185	21	18	and	and	CCONJ
fcis-17185	21	19	biases	bias	NOUN
fcis-17185	21	20	of	of	ADP
fcis-17185	21	21	the	the	DET
fcis-17185	21	22	hidden	hide	VERB
fcis-17185	21	23	layer	layer	NOUN
fcis-17185	21	24	in	in	ADP
fcis-17185	21	25	elm	elm	PROPN
fcis-17185	21	26	.	.	PUNCT
fcis-17185	22	1	liu	liu	PROPN
fcis-17185	22	2	et	et	PROPN
fcis-17185	22	3	al	al	PROPN
fcis-17185	22	4	.	.	PUNCT
fcis-17185	23	1	[	[	X
fcis-17185	23	2	9	9	NUM
fcis-17185	23	3	]	]	PUNCT
fcis-17185	23	4	combined	combine	VERB
fcis-17185	23	5	the	the	DET
fcis-17185	23	6	flower	flower	NOUN
fcis-17185	23	7	pollination	pollination	NOUN
fcis-17185	23	8	algorithm	algorithm	NOUN
fcis-17185	23	9	(	(	PUNCT
fcis-17185	23	10	fa	fa	NOUN
fcis-17185	23	11	)	)	PUNCT
fcis-17185	23	12	and	and	CCONJ
fcis-17185	23	13	firefly	firefly	NOUN
fcis-17185	23	14	algorithm	algorithm	NOUN
fcis-17185	23	15	(	(	PUNCT
fcis-17185	23	16	fpa	fpa	PROPN
fcis-17185	23	17	)	)	PUNCT
fcis-17185	23	18	to	to	PART
fcis-17185	23	19	optimize	optimize	VERB
fcis-17185	23	20	the	the	DET
fcis-17185	23	21	input	input	NOUN
fcis-17185	23	22	layer	layer	NOUN
fcis-17185	23	23	parameters	parameter	NOUN
fcis-17185	23	24	of	of	ADP
fcis-17185	23	25	elm	elm	PROPN
fcis-17185	23	26	.	.	PUNCT
fcis-17185	24	1	they	they	PRON
fcis-17185	24	2	conducted	conduct	VERB
fcis-17185	24	3	numerical	numerical	ADJ
fcis-17185	24	4	experiments	experiment	NOUN
fcis-17185	24	5	to	to	PART
fcis-17185	24	6	validate	validate	VERB
fcis-17185	24	7	the	the	DET
fcis-17185	24	8	network	network	NOUN
fcis-17185	24	9	's	's	PART
fcis-17185	24	10	excellent	excellent	ADJ
fcis-17185	24	11	generalization	generalization	NOUN
fcis-17185	24	12	performance	performance	NOUN
fcis-17185	24	13	even	even	ADV
fcis-17185	24	14	with	with	ADP
fcis-17185	24	15	fewer	few	ADJ
fcis-17185	24	16	parameters	parameter	NOUN
fcis-17185	24	17	and	and	CCONJ
fcis-17185	24	18	a	a	DET
fcis-17185	24	19	simpler	simple	ADJ
fcis-17185	24	20	network	network	NOUN
fcis-17185	24	21	structure	structure	NOUN
fcis-17185	24	22	.	.	PUNCT
fcis-17185	25	1	derya	derya	PROPN
fcis-17185	25	2	et	et	PROPN
fcis-17185	25	3	al	al	PROPN
fcis-17185	25	4	.	.	PUNCT
fcis-17185	26	1	[	[	X
fcis-17185	26	2	10	10	NUM
fcis-17185	26	3	]	]	PUNCT
fcis-17185	26	4	applied	apply	VERB
fcis-17185	26	5	wavelet	wavelet	NOUN
fcis-17185	26	6	kernel	kernel	PROPN
fcis-17185	26	7	functions	function	NOUN
fcis-17185	26	8	to	to	PART
fcis-17185	26	9	elm	elm	VERB
fcis-17185	26	10	and	and	CCONJ
fcis-17185	26	11	proposed	propose	VERB
fcis-17185	26	12	a	a	DET
fcis-17185	26	13	genetic	genetic	ADJ
fcis-17185	26	14	algorithmwavelet	algorithmwavelet	NOUN
fcis-17185	26	15	kernel	kernel	NOUN
fcis-17185	26	16	-	-	PUNCT
fcis-17185	26	17	extreme	extreme	ADJ
fcis-17185	26	18	learning	learning	NOUN
fcis-17185	26	19	machine	machine	NOUN
fcis-17185	26	20	(	(	PUNCT
fcis-17185	26	21	ga	ga	NOUN
fcis-17185	26	22	-	-	PUNCT
fcis-17185	26	23	wk	wk	PROPN
fcis-17185	26	24	-	-	PUNCT
fcis-17185	26	25	elm	elm	NOUN
fcis-17185	26	26	)	)	PUNCT
fcis-17185	26	27	method	method	NOUN
fcis-17185	26	28	,	,	PUNCT
fcis-17185	26	29	which	which	PRON
fcis-17185	26	30	was	be	AUX
fcis-17185	26	31	used	use	VERB
fcis-17185	26	32	for	for	ADP
fcis-17185	26	33	parkinson	parkinson	NOUN
fcis-17185	26	34	's	's	PART
fcis-17185	26	35	disease	disease	NOUN
fcis-17185	26	36	diagnosis	diagnosis	NOUN
fcis-17185	26	37	.	.	PUNCT
fcis-17185	27	1	the	the	DET
fcis-17185	27	2	aforementioned	aforementioned	ADJ
fcis-17185	27	3	papers	paper	NOUN
fcis-17185	27	4	all	all	DET
fcis-17185	27	5	utilized	utilize	VERB
fcis-17185	27	6	swarm	swarm	NOUN
fcis-17185	27	7	intelligence	intelligence	NOUN
fcis-17185	27	8	algorithms	algorithm	NOUN
fcis-17185	27	9	to	to	PART
fcis-17185	27	10	optimize	optimize	VERB
fcis-17185	27	11	the	the	DET
fcis-17185	27	12	input	input	NOUN
fcis-17185	27	13	weights	weight	NOUN
fcis-17185	27	14	and	and	CCONJ
fcis-17185	27	15	thresholds	threshold	NOUN
fcis-17185	27	16	of	of	ADP
fcis-17185	27	17	elm	elm	PROPN
fcis-17185	27	18	,	,	PUNCT
fcis-17185	27	19	thereby	thereby	ADV
fcis-17185	27	20	improving	improve	VERB
fcis-17185	27	21	the	the	DET
fcis-17185	27	22	network	network	NOUN
fcis-17185	27	23	's	's	PART
fcis-17185	27	24	generalization	generalization	NOUN
fcis-17185	27	25	ability	ability	NOUN
fcis-17185	27	26	.	.	PUNCT
fcis-17185	28	1	however	however	ADV
fcis-17185	28	2	,	,	PUNCT
fcis-17185	28	3	this	this	PRON
fcis-17185	28	4	also	also	ADV
fcis-17185	28	5	increased	increase	VERB
fcis-17185	28	6	the	the	DET
fcis-17185	28	7	algorithm	algorithm	NOUN
fcis-17185	28	8	's	's	PART
fcis-17185	28	9	complexity	complexity	NOUN
fcis-17185	28	10	and	and	CCONJ
fcis-17185	28	11	training	training	NOUN
fcis-17185	28	12	time	time	NOUN
fcis-17185	28	13	.	.	PUNCT
fcis-17185	29	1	in	in	ADP
fcis-17185	29	2	2016	2016	NUM
fcis-17185	29	3	,	,	PUNCT
fcis-17185	29	4	seyedali	seyedali	PROPN
fcis-17185	29	5	mirjalili	mirjalili	NOUN
fcis-17185	29	6	proposed	propose	VERB
fcis-17185	29	7	the	the	DET
fcis-17185	29	8	salp	salp	PROPN
fcis-17185	29	9	swarm	swarm	NOUN
fcis-17185	29	10	algorithm	algorithm	NOUN
fcis-17185	29	11	(	(	PUNCT
fcis-17185	29	12	ssa	ssa	NOUN
fcis-17185	29	13	)	)	PUNCT
fcis-17185	30	1	[	[	X
fcis-17185	30	2	11	11	NUM
fcis-17185	30	3	]	]	PUNCT
fcis-17185	30	4	,	,	PUNCT
fcis-17185	30	5	inspired	inspire	VERB
fcis-17185	30	6	by	by	ADP
fcis-17185	30	7	the	the	DET
fcis-17185	30	8	natural	natural	ADJ
fcis-17185	30	9	foraging	foraging	NOUN
fcis-17185	30	10	and	and	CCONJ
fcis-17185	30	11	migration	migration	NOUN
fcis-17185	30	12	behavior	behavior	NOUN
fcis-17185	30	13	of	of	ADP
fcis-17185	30	14	salp	salp	PROPN
fcis-17185	30	15	organisms	organism	NOUN
fcis-17185	30	16	.	.	PUNCT
fcis-17185	31	1	ssa	ssa	NOUN
fcis-17185	31	2	simulates	simulate	VERB
fcis-17185	31	3	the	the	DET
fcis-17185	31	4	predation	predation	NOUN
fcis-17185	31	5	behavior	behavior	NOUN
fcis-17185	31	6	of	of	ADP
fcis-17185	31	7	salps	salps	PROPN
fcis-17185	31	8	,	,	PUNCT
fcis-17185	31	9	considering	consider	VERB
fcis-17185	31	10	the	the	DET
fcis-17185	31	11	ocean	ocean	NOUN
fcis-17185	31	12	as	as	ADP
fcis-17185	31	13	the	the	DET
fcis-17185	31	14	solution	solution	NOUN
fcis-17185	31	15	space	space	NOUN
fcis-17185	31	16	,	,	PUNCT
fcis-17185	31	17	and	and	CCONJ
fcis-17185	31	18	randomly	randomly	ADV
fcis-17185	31	19	generates	generate	VERB
fcis-17185	31	20	a	a	DET
fcis-17185	31	21	population	population	NOUN
fcis-17185	31	22	of	of	ADP
fcis-17185	31	23	salps	salps	ADV
fcis-17185	31	24	to	to	PART
fcis-17185	31	25	search	search	VERB
fcis-17185	31	26	for	for	ADP
fcis-17185	31	27	the	the	DET
fcis-17185	31	28	optimal	optimal	ADJ
fcis-17185	31	29	solution	solution	NOUN
fcis-17185	31	30	.	.	PUNCT
fcis-17185	32	1	compared	compare	VERB
fcis-17185	32	2	to	to	ADP
fcis-17185	32	3	other	other	ADJ
fcis-17185	32	4	swarm	swarm	NOUN
fcis-17185	32	5	intelligence	intelligence	NOUN
fcis-17185	32	6	algorithms	algorithm	NOUN
fcis-17185	32	7	,	,	PUNCT
fcis-17185	32	8	ssa	ssa	PROPN
fcis-17185	32	9	has	have	VERB
fcis-17185	32	10	advantages	advantage	NOUN
fcis-17185	32	11	such	such	ADJ
fcis-17185	32	12	as	as	ADP
fcis-17185	32	13	fast	fast	ADJ
fcis-17185	32	14	convergence	convergence	NOUN
fcis-17185	32	15	,	,	PUNCT
fcis-17185	32	16	strong	strong	ADJ
fcis-17185	32	17	adaptability	adaptability	NOUN
fcis-17185	32	18	,	,	PUNCT
fcis-17185	32	19	and	and	CCONJ
fcis-17185	32	20	robustness	robustness	NOUN
fcis-17185	32	21	,	,	PUNCT
fcis-17185	32	22	making	make	VERB
fcis-17185	32	23	it	it	PRON
fcis-17185	32	24	suitable	suitable	ADJ
fcis-17185	32	25	for	for	ADP
fcis-17185	32	26	optimizing	optimize	VERB
fcis-17185	32	27	the	the	DET
fcis-17185	32	28	elm	elm	PROPN
fcis-17185	32	29	model	model	NOUN
fcis-17185	32	30	.	.	PUNCT
fcis-17185	33	1	tu	tu	PROPN
fcis-17185	33	2	et	et	PROPN
fcis-17185	33	3	al	al	PROPN
fcis-17185	33	4	.	.	PUNCT
fcis-17185	34	1	[	[	X
fcis-17185	34	2	12	12	NUM
fcis-17185	34	3	]	]	PUNCT
fcis-17185	34	4	incorporated	incorporate	VERB
fcis-17185	34	5	the	the	DET
fcis-17185	34	6	harris	harris	PROPN
fcis-17185	34	7	hawk	hawk	PROPN
fcis-17185	34	8	algorithm	algorithm	PROPN
fcis-17185	34	9	into	into	ADP
fcis-17185	34	10	ssa	ssa	PROPN
fcis-17185	34	11	to	to	PART
fcis-17185	34	12	obtain	obtain	VERB
fcis-17185	34	13	an	an	DET
fcis-17185	34	14	excellent	excellent	ADJ
fcis-17185	34	15	population	population	NOUN
fcis-17185	34	16	and	and	CCONJ
fcis-17185	34	17	modified	modify	VERB
fcis-17185	34	18	the	the	DET
fcis-17185	34	19	follower	follower	NOUN
fcis-17185	34	20	's	's	PART
fcis-17185	34	21	position	position	NOUN
fcis-17185	34	22	update	update	NOUN
fcis-17185	34	23	formula	formula	NOUN
fcis-17185	34	24	using	use	VERB
fcis-17185	34	25	the	the	DET
fcis-17185	34	26	multiverse	multiverse	NOUN
fcis-17185	34	27	algorithm	algorithm	NOUN
fcis-17185	34	28	,	,	PUNCT
fcis-17185	34	29	allowing	allow	VERB
fcis-17185	34	30	individual	individual	ADJ
fcis-17185	34	31	salps	salp	NOUN
fcis-17185	34	32	to	to	PART
fcis-17185	34	33	perform	perform	VERB
fcis-17185	34	34	global	global	ADJ
fcis-17185	34	35	and	and	CCONJ
fcis-17185	34	36	local	local	ADJ
fcis-17185	34	37	searches	search	NOUN
fcis-17185	34	38	in	in	ADP
fcis-17185	34	39	different	different	ADJ
fcis-17185	34	40	ranges	range	NOUN
fcis-17185	34	41	.	.	PUNCT
fcis-17185	35	1	the	the	DET
fcis-17185	35	2	improved	improved	ADJ
fcis-17185	35	3	algorithm	algorithm	NOUN
fcis-17185	35	4	was	be	AUX
fcis-17185	35	5	applied	apply	VERB
fcis-17185	35	6	to	to	ADP
fcis-17185	35	7	parameter	parameter	NOUN
fcis-17185	35	8	optimization	optimization	NOUN
fcis-17185	35	9	of	of	ADP
fcis-17185	35	10	elm	elm	NOUN
fcis-17185	35	11	weights	weight	NOUN
fcis-17185	35	12	and	and	CCONJ
fcis-17185	35	13	thresholds	threshold	NOUN
fcis-17185	35	14	.	.	PUNCT
fcis-17185	36	1	although	although	SCONJ
fcis-17185	36	2	the	the	DET
fcis-17185	36	3	aforementioned	aforementioned	ADJ
fcis-17185	36	4	improved	improved	ADJ
fcis-17185	36	5	algorithms	algorithm	NOUN
fcis-17185	36	6	have	have	AUX
fcis-17185	36	7	achieved	achieve	VERB
fcis-17185	36	8	some	some	DET
fcis-17185	36	9	degree	degree	NOUN
fcis-17185	36	10	of	of	ADP
fcis-17185	36	11	enhancement	enhancement	NOUN
fcis-17185	36	12	in	in	ADP
fcis-17185	36	13	the	the	DET
fcis-17185	36	14	search	search	NOUN
fcis-17185	36	15	performance	performance	NOUN
fcis-17185	36	16	of	of	ADP
fcis-17185	36	17	the	the	DET
fcis-17185	36	18	salp	salp	NOUN
fcis-17185	36	19	chain	chain	NOUN
fcis-17185	36	20	,	,	PUNCT
fcis-17185	36	21	they	they	PRON
fcis-17185	36	22	still	still	ADV
fcis-17185	36	23	fail	fail	VERB
fcis-17185	36	24	to	to	PART
fcis-17185	36	25	fully	fully	ADV
fcis-17185	36	26	balance	balance	VERB
fcis-17185	36	27	the	the	DET
fcis-17185	36	28	algorithm	algorithm	NOUN
fcis-17185	36	29	's	's	PART
fcis-17185	36	30	exploration	exploration	NOUN
fcis-17185	36	31	and	and	CCONJ
fcis-17185	36	32	exploitation	exploitation	NOUN
fcis-17185	36	33	capabilities	capability	NOUN
fcis-17185	36	34	to	to	PART
fcis-17185	36	35	achieve	achieve	VERB
fcis-17185	36	36	optimal	optimal	ADJ
fcis-17185	36	37	performance	performance	NOUN
fcis-17185	36	38	.	.	PUNCT
fcis-17185	37	1	therefore	therefore	ADV
fcis-17185	37	2	,	,	PUNCT
fcis-17185	37	3	to	to	PART
fcis-17185	37	4	better	well	ADV
fcis-17185	37	5	balance	balance	VERB
fcis-17185	37	6	the	the	DET
fcis-17185	37	7	exploration	exploration	NOUN
fcis-17185	37	8	and	and	CCONJ
fcis-17185	37	9	exploitation	exploitation	NOUN
fcis-17185	37	10	capabilities	capability	NOUN
fcis-17185	37	11	of	of	ADP
fcis-17185	37	12	ssa	ssa	NOUN
fcis-17185	37	13	,	,	PUNCT
fcis-17185	37	14	this	this	DET
fcis-17185	37	15	paper	paper	NOUN
fcis-17185	37	16	proposes	propose	VERB
fcis-17185	37	17	the	the	DET
fcis-17185	37	18	twin	twin	ADJ
fcis-17185	37	19	strategy	strategy	NOUN
fcis-17185	37	20	salp	salp	NOUN
fcis-17185	37	21	swarm	swarm	NOUN
fcis-17185	37	22	algorithm	algorithm	NOUN
fcis-17185	37	23	(	(	PUNCT
fcis-17185	37	24	tssa	tssa	NOUN
fcis-17185	37	25	)	)	PUNCT
fcis-17185	37	26	.	.	PUNCT
fcis-17185	38	1	firstly	firstly	ADV
fcis-17185	38	2	,	,	PUNCT
fcis-17185	38	3	the	the	DET
fcis-17185	38	4	excellent	excellent	ADJ
fcis-17185	38	5	traversal	traversal	NOUN
fcis-17185	38	6	capability	capability	NOUN
fcis-17185	38	7	of	of	ADP
fcis-17185	38	8	the	the	DET
fcis-17185	38	9	cubic	cubic	ADJ
fcis-17185	38	10	chaotic	chaotic	ADJ
fcis-17185	38	11	mapping	mapping	NOUN
fcis-17185	38	12	is	be	AUX
fcis-17185	38	13	utilized	utilize	VERB
fcis-17185	38	14	by	by	ADP
fcis-17185	38	15	introducing	introduce	VERB
fcis-17185	38	16	it	it	PRON
fcis-17185	38	17	into	into	ADP
fcis-17185	38	18	the	the	DET
fcis-17185	38	19	population	population	NOUN
fcis-17185	38	20	initialization	initialization	NOUN
fcis-17185	38	21	process	process	NOUN
fcis-17185	38	22	,	,	PUNCT
fcis-17185	38	23	allowing	allow	VERB
fcis-17185	38	24	the	the	DET
fcis-17185	38	25	population	population	NOUN
fcis-17185	38	26	to	to	PART
fcis-17185	38	27	obtain	obtain	VERB
fcis-17185	38	28	better	well	ADJ
fcis-17185	38	29	initial	initial	ADJ
fcis-17185	38	30	positions	position	NOUN
fcis-17185	38	31	.	.	PUNCT
fcis-17185	39	1	secondly	secondly	ADV
fcis-17185	39	2	,	,	PUNCT
fcis-17185	39	3	a	a	DET
fcis-17185	39	4	dual	dual	ADJ
fcis-17185	39	5	-	-	PUNCT
fcis-17185	39	6	strategy	strategy	NOUN
fcis-17185	39	7	mechanism	mechanism	NOUN
fcis-17185	39	8	is	be	AUX
fcis-17185	39	9	employed	employ	VERB
fcis-17185	39	10	to	to	PART
fcis-17185	39	11	update	update	VERB
fcis-17185	39	12	the	the	DET
fcis-17185	39	13	follower	follower	NOUN
fcis-17185	39	14	's	's	PART
fcis-17185	39	15	position	position	NOUN
fcis-17185	39	16	information	information	NOUN
fcis-17185	39	17	,	,	PUNCT
fcis-17185	39	18	incorporating	incorporate	VERB
fcis-17185	39	19	oscillating	oscillating	NOUN
fcis-17185	39	20	inertia	inertia	NOUN
fcis-17185	39	21	weight	weight	NOUN
fcis-17185	39	22	and	and	CCONJ
fcis-17185	39	23	learning	learn	VERB
fcis-17185	39	24	patterns	pattern	NOUN
fcis-17185	39	25	to	to	PART
fcis-17185	39	26	balance	balance	VERB
fcis-17185	39	27	local	local	ADJ
fcis-17185	39	28	and	and	CCONJ
fcis-17185	39	29	global	global	ADJ
fcis-17185	39	30	search	search	NOUN
fcis-17185	39	31	,	,	PUNCT
fcis-17185	39	32	improve	improve	VERB
fcis-17185	39	33	the	the	DET
fcis-17185	39	34	ability	ability	NOUN
fcis-17185	39	35	to	to	PART
fcis-17185	39	36	escape	escape	VERB
fcis-17185	39	37	from	from	ADP
fcis-17185	39	38	local	local	ADJ
fcis-17185	39	39	optima	optima	NOUN
fcis-17185	39	40	,	,	PUNCT
fcis-17185	39	41	and	and	CCONJ
fcis-17185	39	42	enhance	enhance	VERB
fcis-17185	39	43	the	the	DET
fcis-17185	39	44	optimization	optimization	NOUN
fcis-17185	39	45	performance	performance	NOUN
fcis-17185	39	46	of	of	ADP
fcis-17185	39	47	the	the	DET
fcis-17185	39	48	algorithm	algorithm	NOUN
fcis-17185	39	49	.	.	PUNCT
fcis-17185	40	1	the	the	DET
fcis-17185	40	2	remaining	remain	VERB
fcis-17185	40	3	sections	section	NOUN
fcis-17185	40	4	of	of	ADP
fcis-17185	40	5	this	this	DET
fcis-17185	40	6	paper	paper	NOUN
fcis-17185	40	7	are	be	AUX
fcis-17185	40	8	organized	organize	VERB
fcis-17185	40	9	as	as	SCONJ
fcis-17185	40	10	follows	follow	VERB
fcis-17185	40	11	.	.	PUNCT
fcis-17185	41	1	section	section	NOUN
fcis-17185	41	2	2	2	NUM
fcis-17185	41	3	provides	provide	VERB
fcis-17185	41	4	an	an	DET
fcis-17185	41	5	introduction	introduction	NOUN
fcis-17185	41	6	to	to	ADP
fcis-17185	41	7	elm	elm	NOUN
fcis-17185	41	8	and	and	CCONJ
fcis-17185	41	9	ssa	ssa	PROPN
fcis-17185	41	10	.	.	PROPN
fcis-17185	41	11	section	section	PROPN
fcis-17185	41	12	3	3	NUM
fcis-17185	41	13	elaborates	elaborate	VERB
fcis-17185	41	14	on	on	ADP
fcis-17185	41	15	the	the	DET
fcis-17185	41	16	process	process	NOUN
fcis-17185	41	17	of	of	ADP
fcis-17185	41	18	improving	improve	VERB
fcis-17185	41	19	the	the	DET
fcis-17185	41	20	salp	salp	NOUN
fcis-17185	41	21	swarm	swarm	NOUN
fcis-17185	41	22	algorithm	algorithm	NOUN
fcis-17185	41	23	.	.	PUNCT
fcis-17185	42	1	section	section	NOUN
fcis-17185	42	2	4	4	NUM
fcis-17185	42	3	outlines	outline	VERB
fcis-17185	42	4	the	the	DET
fcis-17185	42	5	model	model	NOUN
fcis-17185	42	6	construction	construction	NOUN
fcis-17185	42	7	process	process	NOUN
fcis-17185	42	8	.	.	PUNCT
fcis-17185	43	1	section	section	NOUN
fcis-17185	43	2	5	5	NUM
fcis-17185	43	3	analyzes	analyze	VERB
fcis-17185	43	4	the	the	DET
fcis-17185	43	5	experimental	experimental	ADJ
fcis-17185	43	6	results	result	NOUN
fcis-17185	43	7	,	,	PUNCT
fcis-17185	43	8	65	65	NUM
fcis-17185	43	9	demonstrating	demonstrate	VERB
fcis-17185	43	10	the	the	DET
fcis-17185	43	11	effectiveness	effectiveness	NOUN
fcis-17185	43	12	of	of	ADP
fcis-17185	43	13	the	the	DET
fcis-17185	43	14	algorithm	algorithm	NOUN
fcis-17185	43	15	.	.	PUNCT
fcis-17185	44	1	2	2	X
fcis-17185	44	2	.	.	X
fcis-17185	44	3	related	relate	VERB
fcis-17185	44	4	work	work	NOUN
fcis-17185	44	5	(	(	PUNCT
fcis-17185	44	6	1	1	X
fcis-17185	44	7	)	)	PUNCT
fcis-17185	44	8	extreme	extreme	ADJ
fcis-17185	44	9	learning	learning	NOUN
fcis-17185	44	10	machine	machine	NOUN
fcis-17185	44	11	extreme	extreme	ADJ
fcis-17185	44	12	learning	learning	NOUN
fcis-17185	44	13	machine	machine	NOUN
fcis-17185	44	14	is	be	AUX
fcis-17185	44	15	a	a	DET
fcis-17185	44	16	type	type	NOUN
fcis-17185	44	17	of	of	ADP
fcis-17185	44	18	single	single	ADJ
fcis-17185	44	19	-	-	PUNCT
fcis-17185	44	20	hidden	hide	VERB
fcis-17185	44	21	-	-	PUNCT
fcis-17185	44	22	layer	layer	NOUN
fcis-17185	44	23	feedforward	feedforward	NOUN
fcis-17185	44	24	neural	neural	ADJ
fcis-17185	44	25	network	network	NOUN
fcis-17185	44	26	.	.	PUNCT
fcis-17185	45	1	in	in	ADP
fcis-17185	45	2	elm	elm	PROPN
fcis-17185	45	3	,	,	PUNCT
fcis-17185	45	4	the	the	DET
fcis-17185	45	5	input	input	NOUN
fcis-17185	45	6	weights	weight	NOUN
fcis-17185	45	7	and	and	CCONJ
fcis-17185	45	8	thresholds	threshold	NOUN
fcis-17185	45	9	are	be	AUX
fcis-17185	45	10	assigned	assign	VERB
fcis-17185	45	11	randomly	randomly	ADV
fcis-17185	45	12	.	.	PUNCT
fcis-17185	46	1	unlike	unlike	ADP
fcis-17185	46	2	traditional	traditional	ADJ
fcis-17185	46	3	gradient	gradient	ADJ
fcis-17185	46	4	descent	descent	NOUN
fcis-17185	46	5	methods	method	NOUN
fcis-17185	46	6	,	,	PUNCT
fcis-17185	46	7	elm	elm	NOUN
fcis-17185	46	8	adopts	adopt	VERB
fcis-17185	46	9	the	the	DET
fcis-17185	46	10	framework	framework	NOUN
fcis-17185	46	11	of	of	ADP
fcis-17185	46	12	least	least	ADJ
fcis-17185	46	13	squares	square	NOUN
fcis-17185	46	14	to	to	PART
fcis-17185	46	15	calculate	calculate	VERB
fcis-17185	46	16	the	the	DET
fcis-17185	46	17	optimal	optimal	ADJ
fcis-17185	46	18	output	output	NOUN
fcis-17185	46	19	weights	weight	NOUN
fcis-17185	46	20	by	by	ADP
fcis-17185	46	21	solving	solve	VERB
fcis-17185	46	22	the	the	DET
fcis-17185	46	23	corresponding	correspond	VERB
fcis-17185	46	24	moore	moore	PROPN
fcis-17185	46	25	-	-	PUNCT
fcis-17185	46	26	penrose	penrose	NOUN
fcis-17185	46	27	pseudo	pseudo	NOUN
fcis-17185	46	28	-	-	ADJ
fcis-17185	46	29	inverse	inverse	ADJ
fcis-17185	46	30	matrix	matrix	NOUN
fcis-17185	46	31	.	.	PUNCT
fcis-17185	47	1	as	as	ADP
fcis-17185	47	2	a	a	DET
fcis-17185	47	3	result	result	NOUN
fcis-17185	47	4	,	,	PUNCT
fcis-17185	47	5	elm	elm	NOUN
fcis-17185	47	6	exhibits	exhibit	VERB
fcis-17185	47	7	advantages	advantage	NOUN
fcis-17185	47	8	such	such	ADJ
fcis-17185	47	9	as	as	ADP
fcis-17185	47	10	fast	fast	ADJ
fcis-17185	47	11	convergence	convergence	NOUN
fcis-17185	47	12	and	and	CCONJ
fcis-17185	47	13	a	a	DET
fcis-17185	47	14	reduced	reduced	ADJ
fcis-17185	47	15	likelihood	likelihood	NOUN
fcis-17185	47	16	of	of	ADP
fcis-17185	47	17	getting	getting	AUX
fcis-17185	47	18	trapped	trap	VERB
fcis-17185	47	19	in	in	ADP
fcis-17185	47	20	local	local	ADJ
fcis-17185	47	21	optima	optima	NOUN
fcis-17185	47	22	.	.	PUNCT
fcis-17185	48	1	this	this	DET
fcis-17185	48	2	article	article	NOUN
fcis-17185	48	3	believes	believe	VERB
fcis-17185	48	4	that	that	SCONJ
fcis-17185	48	5	the	the	DET
fcis-17185	48	6	concept	concept	NOUN
fcis-17185	48	7	of	of	ADP
fcis-17185	48	8	fintech	fintech	NOUN
fcis-17185	48	9	can	can	AUX
fcis-17185	48	10	be	be	AUX
fcis-17185	48	11	discussed	discuss	VERB
fcis-17185	48	12	from	from	ADP
fcis-17185	48	13	two	two	NUM
fcis-17185	48	14	aspects	aspect	NOUN
fcis-17185	48	15	:	:	PUNCT
fcis-17185	48	16	on	on	ADP
fcis-17185	48	17	the	the	DET
fcis-17185	48	18	one	one	NUM
fcis-17185	48	19	hand	hand	NOUN
fcis-17185	48	20	,	,	PUNCT
fcis-17185	48	21	fintech	fintech	NOUN
fcis-17185	48	22	has	have	AUX
fcis-17185	48	23	emerged	emerge	VERB
fcis-17185	48	24	in	in	ADP
fcis-17185	48	25	product	product	NOUN
fcis-17185	48	26	innovation	innovation	NOUN
fcis-17185	48	27	and	and	CCONJ
fcis-17185	48	28	traditional	traditional	ADJ
fcis-17185	48	29	financial	financial	ADJ
fcis-17185	48	30	services	service	NOUN
fcis-17185	48	31	,	,	PUNCT
fcis-17185	48	32	ultimately	ultimately	ADV
fcis-17185	48	33	still	still	ADV
fcis-17185	48	34	being	be	AUX
fcis-17185	48	35	finance	finance	NOUN
fcis-17185	48	36	.	.	PUNCT
fcis-17185	49	1	on	on	ADP
fcis-17185	49	2	the	the	DET
fcis-17185	49	3	other	other	ADJ
fcis-17185	49	4	hand	hand	NOUN
fcis-17185	49	5	,	,	PUNCT
fcis-17185	49	6	financial	financial	ADJ
fcis-17185	49	7	technology	technology	NOUN
fcis-17185	49	8	is	be	AUX
fcis-17185	49	9	a	a	DET
fcis-17185	49	10	technological	technological	ADJ
fcis-17185	49	11	means	mean	NOUN
fcis-17185	49	12	derived	derive	VERB
fcis-17185	49	13	from	from	ADP
fcis-17185	49	14	the	the	DET
fcis-17185	49	15	development	development	NOUN
fcis-17185	49	16	of	of	ADP
fcis-17185	49	17	modern	modern	ADJ
fcis-17185	49	18	technology	technology	NOUN
fcis-17185	49	19	that	that	PRON
fcis-17185	49	20	specifically	specifically	ADV
fcis-17185	49	21	serves	serve	VERB
fcis-17185	49	22	the	the	DET
fcis-17185	49	23	financial	financial	ADJ
fcis-17185	49	24	industry	industry	NOUN
fcis-17185	49	25	.	.	PUNCT
fcis-17185	50	1	financial	financial	ADJ
fcis-17185	50	2	technology	technology	NOUN
fcis-17185	50	3	has	have	VERB
fcis-17185	50	4	a	a	DET
fcis-17185	50	5	very	very	ADV
fcis-17185	50	6	powerful	powerful	ADJ
fcis-17185	50	7	technological	technological	ADJ
fcis-17185	50	8	gene	gene	NOUN
fcis-17185	50	9	that	that	PRON
fcis-17185	50	10	can	can	AUX
fcis-17185	50	11	empower	empower	VERB
fcis-17185	50	12	the	the	DET
fcis-17185	50	13	financial	financial	ADJ
fcis-17185	50	14	industry	industry	NOUN
fcis-17185	50	15	to	to	PART
fcis-17185	50	16	improve	improve	VERB
fcis-17185	50	17	quality	quality	NOUN
fcis-17185	50	18	and	and	CCONJ
fcis-17185	50	19	efficiency	efficiency	NOUN
fcis-17185	50	20	.	.	PUNCT
fcis-17185	51	1	figure	figure	NOUN
fcis-17185	51	2	1	1	NUM
fcis-17185	51	3	.	.	PUNCT
fcis-17185	52	1	extreme	extreme	ADJ
fcis-17185	52	2	learning	learning	NOUN
fcis-17185	52	3	machine	machine	NOUN
fcis-17185	52	4	the	the	DET
fcis-17185	52	5	topological	topological	ADJ
fcis-17185	52	6	structure	structure	NOUN
fcis-17185	52	7	of	of	ADP
fcis-17185	52	8	the	the	DET
fcis-17185	52	9	extreme	extreme	ADJ
fcis-17185	52	10	learning	learning	NOUN
fcis-17185	52	11	machine	machine	NOUN
fcis-17185	52	12	withn	withn	PROPN
fcis-17185	52	13	input	input	NOUN
fcis-17185	52	14	nodes	node	NOUN
fcis-17185	52	15	,	,	PUNCT
fcis-17185	52	16	l	l	NOUN
fcis-17185	52	17	hidden	hide	VERB
fcis-17185	52	18	nodes	node	NOUN
fcis-17185	52	19	,	,	PUNCT
fcis-17185	52	20	andm	andm	PROPN
fcis-17185	52	21	output	output	NOUN
fcis-17185	52	22	nodes	node	NOUN
fcis-17185	52	23	is	be	AUX
fcis-17185	52	24	illustrated	illustrate	VERB
fcis-17185	52	25	in	in	ADP
fcis-17185	52	26	figure	figure	NOUN
fcis-17185	52	27	1	1	NUM
fcis-17185	52	28	.	.	PUNCT
fcis-17185	53	1	let	let	VERB
fcis-17185	53	2	's	us	PRON
fcis-17185	53	3	assume	assume	VERB
fcis-17185	53	4	that	that	SCONJ
fcis-17185	53	5	the	the	DET
fcis-17185	53	6	activation	activation	NOUN
fcis-17185	53	7	function	function	VERB
fcis-17185	53	8	for	for	ADP
fcis-17185	53	9	the	the	DET
fcis-17185	53	10	hidden	hide	VERB
fcis-17185	53	11	layer	layer	NOUN
fcis-17185	53	12	is	be	AUX
fcis-17185	53	13	denoted	denote	VERB
fcis-17185	53	14	as	as	ADP
fcis-17185	53	15	g	g	NOUN
fcis-17185	53	16	,	,	PUNCT
fcis-17185	53	17	and	and	CCONJ
fcis-17185	53	18	the	the	DET
fcis-17185	53	19	threshold	threshold	NOUN
fcis-17185	53	20	values	value	NOUN
fcis-17185	53	21	are	be	AUX
fcis-17185	53	22	represented	represent	VERB
fcis-17185	53	23	by	by	ADP
fcis-17185	53	24	b.	b.	PROPN
fcis-17185	53	25	for	for	ADP
fcis-17185	53	26	a	a	DET
fcis-17185	53	27	set	set	VERB
fcis-17185	53	28	ofp	ofp	NOUN
fcis-17185	53	29	different	different	ADJ
fcis-17185	53	30	samples	sample	NOUN
fcis-17185	54	1			NOUN
fcis-17185	54	2			PROPN
fcis-17185	54	3	,	,	PUNCT
fcis-17185	54	4	n	n	PRON
fcis-17185	54	5	m	m	VERB
fcis-17185	54	6	i	i	PRON
fcis-17185	54	7	i	i	PRON
fcis-17185	54	8			NOUN
fcis-17185	54	9	x	x	NOUN
fcis-17185	54	10	o	o	NOUN
fcis-17185	54	11	r	r	NOUN
fcis-17185	54	12	r	r	NOUN
fcis-17185	54	13	,	,	PUNCT
fcis-17185	54	14	where	where	SCONJ
fcis-17185	54	15			ADJ
fcis-17185	54	16	1	1	NOUN
fcis-17185	54	17	2	2	NUM
fcis-17185	54	18	,	,	PUNCT
fcis-17185	54	19	,	,	PUNCT
fcis-17185	54	20	,	,	PUNCT
fcis-17185	54	21	n	n	CCONJ
fcis-17185	54	22	i	i	PRON
fcis-17185	55	1	i	i	PRON
fcis-17185	55	2	i	i	PRON
fcis-17185	55	3	inx	inx	VERB
fcis-17185	55	4	x	x	PUNCT
fcis-17185	55	5	x	x	PROPN
fcis-17185	55	6	x	x	NOUN
fcis-17185	55	7	r	r	PROPN
fcis-17185	55	8	denotes	denote	NOUN
fcis-17185	55	9	the	the	DET
fcis-17185	55	10	n	n	CCONJ
fcis-17185	55	11	-dimensional	-dimensional	ADJ
fcis-17185	55	12	input	input	NOUN
fcis-17185	55	13	samples	sample	NOUN
fcis-17185	55	14	and	and	CCONJ
fcis-17185	55	15			ADJ
fcis-17185	55	16	1	1	NOUN
fcis-17185	55	17	2	2	NUM
fcis-17185	55	18	,	,	PUNCT
fcis-17185	55	19	,	,	PUNCT
fcis-17185	55	20	,	,	PUNCT
fcis-17185	55	21	m	m	VERB
fcis-17185	55	22	i	i	INTJ
fcis-17185	56	1	i	i	PRON
fcis-17185	57	1	i	i	PRON
fcis-17185	57	2	imo	imo	ADV
fcis-17185	58	1	o	o	INTJ
fcis-17185	58	2	o	o	PUNCT
fcis-17185	58	3	o	o	X
fcis-17185	58	4	r	r	ADV
fcis-17185	58	5	represents	represent	VERB
fcis-17185	58	6	the	the	DET
fcis-17185	58	7	desired	desire	VERB
fcis-17185	58	8	output	output	NOUN
fcis-17185	58	9	of	of	ADP
fcis-17185	58	10	the	the	DET
fcis-17185	58	11	model	model	NOUN
fcis-17185	58	12	.	.	PUNCT
fcis-17185	59	1	the	the	DET
fcis-17185	59	2	mathematical	mathematical	ADJ
fcis-17185	59	3	model	model	NOUN
fcis-17185	59	4	of	of	ADP
fcis-17185	59	5	the	the	DET
fcis-17185	59	6	elm	elm	NOUN
fcis-17185	59	7	can	can	AUX
fcis-17185	59	8	be	be	AUX
fcis-17185	59	9	expressed	express	VERB
fcis-17185	59	10	as	as	ADP
fcis-17185	59	11	:	:	PUNCT
fcis-17185	59	12			NOUN
fcis-17185	59	13			PROPN
fcis-17185	59	14	1	1	NUM
fcis-17185	59	15	,	,	PUNCT
fcis-17185	59	16	1,2	1,2	NUM
fcis-17185	59	17	,	,	PUNCT
fcis-17185	59	18	,	,	PUNCT
fcis-17185	59	19	n	n	CCONJ
fcis-17185	60	1	i	i	PRON
fcis-17185	60	2	i	i	PRON
fcis-17185	61	1	i	i	PRON
fcis-17185	62	1	i	i	PRON
fcis-17185	62	2	j	j	VERB
fcis-17185	63	1	i	i	PRON
fcis-17185	63	2	g	g	PROPN
fcis-17185	63	3	b	b	PROPN
fcis-17185	63	4	j	j	PROPN
fcis-17185	63	5	m	m	VERB
fcis-17185	63	6			PROPN
fcis-17185	63	7			PROPN
fcis-17185	63	8			ADV
fcis-17185	63	9			NUM
fcis-17185	63	10	β	β	PROPN
fcis-17185	63	11	w	w	PROPN
fcis-17185	63	12	x	x	SYM
fcis-17185	63	13	t	t	NOUN
fcis-17185	63	14			NUM
fcis-17185	63	15	(	(	PUNCT
fcis-17185	63	16	1	1	NUM
fcis-17185	63	17	)	)	PUNCT
fcis-17185	63	18	where	where	SCONJ
fcis-17185	63	19			ADJ
fcis-17185	63	20	1	1	NOUN
fcis-17185	63	21	2	2	NUM
fcis-17185	63	22	,	,	PUNCT
fcis-17185	63	23	,	,	PUNCT
fcis-17185	63	24	,	,	PUNCT
fcis-17185	63	25	t	t	PROPN
fcis-17185	64	1	i	i	PRON
fcis-17185	64	2	i	i	PRON
fcis-17185	65	1	i	i	PRON
fcis-17185	65	2	miw	miw	PROPN
fcis-17185	65	3	w	w	PROPN
fcis-17185	65	4	ww	ww	ADJ
fcis-17185	65	5			NUM
fcis-17185	65	6	denotes	denote	VERB
fcis-17185	65	7	the	the	DET
fcis-17185	65	8	input	input	NOUN
fcis-17185	65	9	weights	weight	VERB
fcis-17185	65	10	pointing	point	VERB
fcis-17185	65	11	to	to	ADP
fcis-17185	65	12	the	the	DET
fcis-17185	65	13	first	first	ADJ
fcis-17185	65	14	hidden	hide	VERB
fcis-17185	65	15	node	node	NOUN
fcis-17185	65	16	,	,	PUNCT
fcis-17185	65	17			ADJ
fcis-17185	65	18	1	1	NOUN
fcis-17185	65	19	2	2	NUM
fcis-17185	65	20	,	,	PUNCT
fcis-17185	65	21	,	,	PUNCT
fcis-17185	65	22	,	,	PUNCT
fcis-17185	65	23	t	t	PROPN
fcis-17185	66	1	i	i	PRON
fcis-17185	67	1	i	i	PRON
fcis-17185	68	1	i	i	PRON
fcis-17185	68	2	im	im	VERB
fcis-17185	68	3			PROPN
fcis-17185	68	4	β	β	NOUN
fcis-17185	68	5			NUM
fcis-17185	68	6	denotes	denote	VERB
fcis-17185	68	7	the	the	DET
fcis-17185	68	8	output	output	NOUN
fcis-17185	68	9	weights	weight	NOUN
fcis-17185	68	10	between	between	ADP
fcis-17185	68	11	the	the	DET
fcis-17185	68	12	first	first	ADJ
fcis-17185	68	13	hidden	hide	VERB
fcis-17185	68	14	layer	layer	NOUN
fcis-17185	68	15	node	node	NOUN
fcis-17185	68	16	and	and	CCONJ
fcis-17185	68	17	the	the	DET
fcis-17185	68	18	output	output	NOUN
fcis-17185	68	19	layer	layer	NOUN
fcis-17185	68	20	,	,	PUNCT
fcis-17185	68	21	and	and	CCONJ
fcis-17185	68	22	it	it	PRON
fcis-17185	68	23	is	be	AUX
fcis-17185	68	24	the	the	DET
fcis-17185	68	25	actual	actual	ADJ
fcis-17185	68	26	output	output	NOUN
fcis-17185	68	27	of	of	ADP
fcis-17185	68	28	the	the	DET
fcis-17185	68	29	network	network	NOUN
fcis-17185	68	30	.	.	PUNCT
fcis-17185	69	1	during	during	ADP
fcis-17185	69	2	the	the	DET
fcis-17185	69	3	training	training	NOUN
fcis-17185	69	4	and	and	CCONJ
fcis-17185	69	5	learning	learning	NOUN
fcis-17185	69	6	process	process	NOUN
fcis-17185	69	7	,	,	PUNCT
fcis-17185	69	8	when	when	SCONJ
fcis-17185	69	9	the	the	DET
fcis-17185	69	10	learning	learning	NOUN
fcis-17185	69	11	error	error	NOUN
fcis-17185	69	12	drops	drop	VERB
fcis-17185	69	13	to	to	ADP
fcis-17185	69	14	0	0	NUM
fcis-17185	69	15	,	,	PUNCT
fcis-17185	69	16	which	which	PRON
fcis-17185	69	17	is	be	AUX
fcis-17185	69	18	1	1	NUM
fcis-17185	69	19	0	0	NUM
fcis-17185	69	20	m	m	NOUN
fcis-17185	69	21	j	j	PROPN
fcis-17185	69	22	j	j	PROPN
fcis-17185	69	23	j	j	PROPN
fcis-17185	69	24			PROPN
fcis-17185	69	25			PROPN
fcis-17185	69	26	t	t	PROPN
fcis-17185	69	27	o	o	NOUN
fcis-17185	69	28	,	,	PUNCT
fcis-17185	69	29	it	it	PRON
fcis-17185	69	30	indicates	indicate	VERB
fcis-17185	69	31	that	that	SCONJ
fcis-17185	69	32	the	the	DET
fcis-17185	69	33	learning	learn	VERB
fcis-17185	69	34	ability	ability	NOUN
fcis-17185	69	35	of	of	ADP
fcis-17185	69	36	the	the	DET
fcis-17185	69	37	extreme	extreme	ADJ
fcis-17185	69	38	learning	learning	NOUN
fcis-17185	69	39	machine	machine	NOUN
fcis-17185	69	40	is	be	AUX
fcis-17185	69	41	optimal	optimal	ADJ
fcis-17185	69	42	at	at	ADP
fcis-17185	69	43	this	this	DET
fcis-17185	69	44	point	point	NOUN
fcis-17185	69	45	,	,	PUNCT
fcis-17185	69	46	which	which	PRON
fcis-17185	69	47	means	mean	VERB
fcis-17185	69	48	that	that	SCONJ
fcis-17185	69	49	there	there	PRON
fcis-17185	69	50	exists	exist	VERB
fcis-17185	69	51	a	a	DET
fcis-17185	69	52	set	set	NOUN
fcis-17185	69	53	of	of	ADP
fcis-17185	69	54			NOUN
fcis-17185	69	55			PROPN
fcis-17185	69	56	,	,	PUNCT
fcis-17185	69	57	,	,	PUNCT
fcis-17185	69	58	i	i	PRON
fcis-17185	69	59	i	i	PRON
fcis-17185	69	60	ibw	ibw	VERB
fcis-17185	69	61	β	β	INTJ
fcis-17185	69	62	such	such	ADJ
fcis-17185	69	63	that	that	SCONJ
fcis-17185	69	64	:	:	PUNCT
fcis-17185	69	65			NOUN
fcis-17185	69	66			PROPN
fcis-17185	69	67	1	1	NUM
fcis-17185	69	68	n	n	NUM
fcis-17185	69	69	j	j	NOUN
fcis-17185	70	1	i	i	PRON
fcis-17185	70	2	i	i	PRON
fcis-17185	71	1	i	i	PRON
fcis-17185	72	1	i	i	PRON
fcis-17185	72	2	j	j	VERB
fcis-17185	73	1	i	i	PRON
fcis-17185	73	2	g	g	PROPN
fcis-17185	74	1	b	b	PROPN
fcis-17185	74	2			PROPN
fcis-17185	74	3			PROPN
fcis-17185	74	4			PROPN
fcis-17185	74	5			PUNCT
fcis-17185	74	6	t	t	PUNCT
fcis-17185	74	7	β	β	X
fcis-17185	74	8	w	w	NOUN
fcis-17185	74	9	x	x	X
fcis-17185	74	10	o	o	X
fcis-17185	74	11	(	(	PUNCT
fcis-17185	74	12	2	2	NUM
fcis-17185	74	13	)	)	PUNCT
fcis-17185	74	14	the	the	PRON
fcis-17185	74	15	above	above	ADP
fcis-17185	74	16	p	p	NOUN
fcis-17185	74	17	equations	equation	NOUN
fcis-17185	74	18	can	can	AUX
fcis-17185	74	19	be	be	AUX
fcis-17185	74	20	written	write	VERB
fcis-17185	74	21	in	in	ADP
fcis-17185	74	22	the	the	DET
fcis-17185	74	23	following	follow	VERB
fcis-17185	74	24	matrix	matrix	NOUN
fcis-17185	74	25	form	form	NOUN
fcis-17185	74	26	:	:	PUNCT
fcis-17185	74	27	hβ	hβ	NUM
fcis-17185	74	28	o	o	NOUN
fcis-17185	74	29	(	(	PUNCT
fcis-17185	74	30	3	3	NUM
fcis-17185	74	31	)	)	PUNCT
fcis-17185	74	32	here	here	ADV
fcis-17185	74	33	,	,	PUNCT
fcis-17185	74	34	h	h	NOUN
fcis-17185	74	35	is	be	AUX
fcis-17185	74	36	called	call	VERB
fcis-17185	74	37	the	the	DET
fcis-17185	74	38	hidden	hide	VERB
fcis-17185	74	39	layer	layer	NOUN
fcis-17185	74	40	output	output	NOUN
fcis-17185	74	41	matrix	matrix	NOUN
fcis-17185	74	42	,	,	PUNCT
fcis-17185	74	43	and	and	CCONJ
fcis-17185	74	44	we	we	PRON
fcis-17185	74	45	aim	aim	VERB
fcis-17185	74	46	to	to	PART
fcis-17185	74	47	obtain	obtain	VERB
fcis-17185	74	48	a	a	DET
fcis-17185	74	49	set	set	NOUN
fcis-17185	74	50	of	of	ADP
fcis-17185	74	51	parameters	parameter	NOUN
fcis-17185	74	52			PROPN
fcis-17185	74	53			PROPN
fcis-17185	74	54	,	,	PUNCT
fcis-17185	74	55	,	,	PUNCT
fcis-17185	74	56	b	b	PROPN
fcis-17185	74	57			PROPN
fcis-17185	75	1	w	w	NOUN
fcis-17185	75	2	β	β	X
fcis-17185	75	3	such	such	ADJ
fcis-17185	75	4	that	that	PRON
fcis-17185	75	5			NOUN
fcis-17185	75	6			SYM
fcis-17185	75	7			NOUN
fcis-17185	75	8			PROPN
fcis-17185	75	9	,	,	PUNCT
fcis-17185	75	10	,	,	PUNCT
fcis-17185	75	11	,	,	PUNCT
fcis-17185	75	12	min	min	PROPN
fcis-17185	75	13	,	,	PUNCT
fcis-17185	75	14	b	b	PROPN
fcis-17185	75	15	b	b	PROPN
fcis-17185	75	16	b	b	PROPN
fcis-17185	75	17			PROPN
fcis-17185	75	18			AUX
fcis-17185	75	19			VERB
fcis-17185	75	20			PROPN
fcis-17185	75	21			PROPN
fcis-17185	75	22	w	w	PROPN
fcis-17185	75	23	β	β	X
fcis-17185	75	24	h	h	NOUN
fcis-17185	75	25	w	w	PROPN
fcis-17185	75	26	β	β	X
fcis-17185	75	27	o	o	NOUN
fcis-17185	75	28	h	h	NOUN
fcis-17185	75	29	w	w	PROPN
fcis-17185	75	30	β	β	X
fcis-17185	75	31	o	o	X
fcis-17185	75	32	(	(	PUNCT
fcis-17185	75	33	4	4	NUM
fcis-17185	75	34	)	)	PUNCT
fcis-17185	75	35	when	when	SCONJ
fcis-17185	75	36	the	the	DET
fcis-17185	75	37	number	number	NOUN
fcis-17185	75	38	of	of	ADP
fcis-17185	75	39	nodes	node	NOUN
fcis-17185	75	40	in	in	ADP
fcis-17185	75	41	the	the	DET
fcis-17185	75	42	hidden	hide	VERB
fcis-17185	75	43	layer	layer	NOUN
fcis-17185	75	44	is	be	AUX
fcis-17185	75	45	equal	equal	ADJ
fcis-17185	75	46	to	to	ADP
fcis-17185	75	47	the	the	DET
fcis-17185	75	48	number	number	NOUN
fcis-17185	75	49	of	of	ADP
fcis-17185	75	50	samples	sample	NOUN
fcis-17185	75	51	in	in	ADP
fcis-17185	75	52	the	the	DET
fcis-17185	75	53	training	training	NOUN
fcis-17185	75	54	set	set	NOUN
fcis-17185	75	55	,	,	PUNCT
fcis-17185	75	56	h	h	NOUN
fcis-17185	75	57	is	be	AUX
fcis-17185	75	58	an	an	DET
fcis-17185	75	59	invertible	invertible	ADJ
fcis-17185	75	60	square	square	ADJ
fcis-17185	75	61	matrix	matrix	NOUN
fcis-17185	75	62	,	,	PUNCT
fcis-17185	75	63	and	and	CCONJ
fcis-17185	75	64	the	the	DET
fcis-17185	75	65	network	network	NOUN
fcis-17185	75	66	can	can	AUX
fcis-17185	75	67	approximate	approximate	VERB
fcis-17185	75	68	the	the	DET
fcis-17185	75	69	training	training	NOUN
fcis-17185	75	70	samples	sample	NOUN
fcis-17185	75	71	with	with	ADP
fcis-17185	75	72	zero	zero	NUM
fcis-17185	75	73	error	error	NOUN
fcis-17185	75	74	,	,	PUNCT
fcis-17185	75	75	but	but	CCONJ
fcis-17185	75	76	in	in	ADP
fcis-17185	75	77	practical	practical	ADJ
fcis-17185	75	78	applications	application	NOUN
fcis-17185	75	79	,	,	PUNCT
fcis-17185	75	80	the	the	DET
fcis-17185	75	81	number	number	NOUN
fcis-17185	75	82	of	of	ADP
fcis-17185	75	83	nodes	node	NOUN
fcis-17185	75	84	in	in	ADP
fcis-17185	75	85	the	the	DET
fcis-17185	75	86	hidden	hide	VERB
fcis-17185	75	87	layer	layer	NOUN
fcis-17185	75	88	is	be	AUX
fcis-17185	75	89	usually	usually	ADV
fcis-17185	75	90	smaller	small	ADJ
fcis-17185	75	91	than	than	ADP
fcis-17185	75	92	the	the	DET
fcis-17185	75	93	number	number	NOUN
fcis-17185	75	94	of	of	ADP
fcis-17185	75	95	training	training	NOUN
fcis-17185	75	96	samples	sample	NOUN
fcis-17185	75	97	.	.	PUNCT
fcis-17185	76	1	therefore	therefore	ADV
fcis-17185	76	2	,	,	PUNCT
fcis-17185	76	3	when	when	SCONJ
fcis-17185	76	4	the	the	DET
fcis-17185	76	5	activation	activation	NOUN
fcis-17185	76	6	function	function	NOUN
fcis-17185	76	7	is	be	AUX
fcis-17185	76	8	infinitely	infinitely	ADV
fcis-17185	76	9	differentiable	differentiable	ADJ
fcis-17185	76	10	,	,	PUNCT
fcis-17185	76	11	the	the	DET
fcis-17185	76	12	output	output	NOUN
fcis-17185	76	13	weights	weight	NOUN
fcis-17185	76	14	can	can	AUX
fcis-17185	76	15	be	be	AUX
fcis-17185	76	16	obtained	obtain	VERB
fcis-17185	76	17	by	by	ADP
fcis-17185	76	18	solving	solve	VERB
fcis-17185	76	19	the	the	DET
fcis-17185	76	20	least	least	ADJ
fcis-17185	76	21	squares	square	NOUN
fcis-17185	76	22	solution	solution	NOUN
fcis-17185	76	23	of	of	ADP
fcis-17185	76	24	the	the	DET
fcis-17185	76	25	linear	linear	ADJ
fcis-17185	76	26	system	system	NOUN
fcis-17185	76	27	hβ	hβ	NOUN
fcis-17185	76	28	owith	owith	ADP
fcis-17185	76	29	the	the	DET
fcis-17185	76	30	explicit	explicit	ADJ
fcis-17185	76	31	solution	solution	NOUN
fcis-17185	76	32	:	:	PUNCT
fcis-17185	76	33	†ˆ	†ˆ	NOUN
fcis-17185	76	34	β	β	PROPN
fcis-17185	76	35	h	h	NOUN
fcis-17185	76	36	o	o	NOUN
fcis-17185	76	37	(	(	PUNCT
fcis-17185	76	38	5	5	NUM
fcis-17185	76	39	)	)	PUNCT
fcis-17185	76	40	where	where	SCONJ
fcis-17185	76	41	is	be	AUX
fcis-17185	76	42	the	the	DET
fcis-17185	76	43	generalized	generalized	ADJ
fcis-17185	76	44	inverse	inverse	NOUN
fcis-17185	76	45	of	of	ADP
fcis-17185	76	46	moore	moore	PROPN
fcis-17185	76	47	-	-	PUNCT
fcis-17185	76	48	penrose	penrose	PROPN
fcis-17185	76	49	.	.	PUNCT
fcis-17185	77	1	(	(	PUNCT
fcis-17185	77	2	2	2	X
fcis-17185	77	3	)	)	PUNCT
fcis-17185	77	4	the	the	DET
fcis-17185	77	5	classic	classic	ADJ
fcis-17185	77	6	salp	salp	NOUN
fcis-17185	77	7	swarm	swarm	NOUN
fcis-17185	77	8	algorithm	algorithm	NOUN
fcis-17185	77	9	the	the	DET
fcis-17185	77	10	salp	salp	PROPN
fcis-17185	77	11	swarm	swarm	NOUN
fcis-17185	77	12	algorithm	algorithm	NOUN
fcis-17185	77	13	(	(	PUNCT
fcis-17185	77	14	ssa	ssa	NOUN
fcis-17185	77	15	)	)	PUNCT
fcis-17185	77	16	is	be	AUX
fcis-17185	77	17	designed	design	VERB
fcis-17185	77	18	based	base	VERB
fcis-17185	77	19	on	on	ADP
fcis-17185	77	20	the	the	DET
fcis-17185	77	21	biological	biological	ADJ
fcis-17185	77	22	characteristics	characteristic	NOUN
fcis-17185	77	23	of	of	ADP
fcis-17185	77	24	salp	salp	NOUN
fcis-17185	77	25	swarms	swarm	NOUN
fcis-17185	77	26	.	.	PUNCT
fcis-17185	78	1	in	in	ADP
fcis-17185	78	2	ssa	ssa	PROPN
fcis-17185	78	3	,	,	PUNCT
fcis-17185	78	4	the	the	DET
fcis-17185	78	5	first	first	ADJ
fcis-17185	78	6	half	half	NOUN
fcis-17185	78	7	of	of	ADP
fcis-17185	78	8	the	the	DET
fcis-17185	78	9	salps	salp	NOUN
fcis-17185	78	10	are	be	AUX
fcis-17185	78	11	leaders	leader	NOUN
fcis-17185	78	12	,	,	PUNCT
fcis-17185	78	13	while	while	SCONJ
fcis-17185	78	14	the	the	DET
fcis-17185	78	15	rest	rest	NOUN
fcis-17185	78	16	are	be	AUX
fcis-17185	78	17	followers	follower	NOUN
fcis-17185	78	18	.	.	PUNCT
fcis-17185	79	1	unlike	unlike	ADP
fcis-17185	79	2	other	other	ADJ
fcis-17185	79	3	swarm	swarm	NOUN
fcis-17185	79	4	intelligence	intelligence	NOUN
fcis-17185	79	5	algorithms	algorithm	NOUN
fcis-17185	79	6	,	,	PUNCT
fcis-17185	79	7	leaders	leader	NOUN
fcis-17185	79	8	do	do	AUX
fcis-17185	79	9	not	not	PART
fcis-17185	79	10	influence	influence	VERB
fcis-17185	79	11	the	the	DET
fcis-17185	79	12	movement	movement	NOUN
fcis-17185	79	13	of	of	ADP
fcis-17185	79	14	the	the	DET
fcis-17185	79	15	entire	entire	ADJ
fcis-17185	79	16	swarm	swarm	NOUN
fcis-17185	79	17	,	,	PUNCT
fcis-17185	79	18	and	and	CCONJ
fcis-17185	79	19	the	the	DET
fcis-17185	79	20	positions	position	NOUN
fcis-17185	79	21	of	of	ADP
fcis-17185	79	22	the	the	DET
fcis-17185	79	23	followers	follower	NOUN
fcis-17185	79	24	are	be	AUX
fcis-17185	79	25	updated	update	VERB
fcis-17185	79	26	based	base	VERB
fcis-17185	79	27	on	on	ADP
fcis-17185	79	28	the	the	DET
fcis-17185	79	29	position	position	NOUN
fcis-17185	79	30	of	of	ADP
fcis-17185	79	31	the	the	DET
fcis-17185	79	32	preceding	precede	VERB
fcis-17185	79	33	individual	individual	NOUN
fcis-17185	79	34	.	.	PUNCT
fcis-17185	80	1	the	the	DET
fcis-17185	80	2	leaders	leader	NOUN
fcis-17185	80	3	actively	actively	ADV
fcis-17185	80	4	search	search	VERB
fcis-17185	80	5	for	for	ADP
fcis-17185	80	6	food	food	NOUN
fcis-17185	80	7	sources	source	NOUN
fcis-17185	80	8	(	(	PUNCT
fcis-17185	80	9	i.e.	i.e.	X
fcis-17185	80	10	,	,	PUNCT
fcis-17185	80	11	optimal	optimal	ADJ
fcis-17185	80	12	solutions	solution	NOUN
fcis-17185	80	13	)	)	PUNCT
fcis-17185	80	14	in	in	ADP
fcis-17185	80	15	ann	ann	PROPN
fcis-17185	80	16	-dimensional	-dimensional	ADJ
fcis-17185	80	17	space	space	NOUN
fcis-17185	80	18	.	.	PUNCT
fcis-17185	81	1	the	the	DET
fcis-17185	81	2	movement	movement	NOUN
fcis-17185	81	3	trajectory	trajectory	NOUN
fcis-17185	81	4	is	be	AUX
fcis-17185	81	5	determined	determine	VERB
fcis-17185	81	6	by	by	ADP
fcis-17185	81	7	the	the	DET
fcis-17185	81	8	following	follow	VERB
fcis-17185	81	9	equation	equation	NOUN
fcis-17185	81	10	:	:	PUNCT
fcis-17185	81	11			PROPN
fcis-17185	81	12			PROPN
fcis-17185	82	1			PROPN
fcis-17185	82	2			PROPN
fcis-17185	82	3			NOUN
fcis-17185	83	1			PROPN
fcis-17185	83	2	1	1	NUM
fcis-17185	83	3	2	2	NUM
fcis-17185	83	4	3	3	NUM
fcis-17185	83	5	1	1	NUM
fcis-17185	83	6	1	1	NUM
fcis-17185	83	7	2	2	NUM
fcis-17185	83	8	3	3	NUM
fcis-17185	83	9	,	,	PUNCT
fcis-17185	83	10	0.5	0.5	NUM
fcis-17185	83	11	,	,	PUNCT
fcis-17185	84	1	0.5	0.5	NUM
fcis-17185	84	2	j	j	PROPN
fcis-17185	84	3	j	j	PROPN
fcis-17185	84	4	j	j	PROPN
fcis-17185	84	5	j	j	PROPN
fcis-17185	84	6	j	j	PROPN
fcis-17185	84	7	j	j	PROPN
fcis-17185	84	8	j	j	PROPN
fcis-17185	85	1	j	j	PROPN
fcis-17185	85	2	j	j	PROPN
fcis-17185	86	1	f	f	PROPN
fcis-17185	86	2	c	c	NOUN
fcis-17185	87	1	c	c	NOUN
fcis-17185	87	2	ub	ub	INTJ
fcis-17185	88	1	lb	lb	NUM
fcis-17185	88	2	lb	lb	PRON
fcis-17185	88	3	c	c	NOUN
fcis-17185	88	4	x	x	X
fcis-17185	89	1	f	f	NOUN
fcis-17185	89	2	c	c	NOUN
fcis-17185	89	3	c	c	NOUN
fcis-17185	89	4	ub	ub	INTJ
fcis-17185	90	1	lb	lb	INTJ
fcis-17185	90	2	lb	lb	PRON
fcis-17185	90	3	c	c	NOUN
fcis-17185	90	4			ADP
fcis-17185	90	5			ADV
fcis-17185	90	6			PROPN
fcis-17185	90	7			CCONJ
fcis-17185	90	8			PROPN
fcis-17185	90	9			NUM
fcis-17185	90	10			PROPN
fcis-17185	90	11			PROPN
fcis-17185	90	12			VERB
fcis-17185	90	13			X
fcis-17185	90	14	(	(	PUNCT
fcis-17185	90	15	6	6	NUM
fcis-17185	90	16	)	)	PUNCT
fcis-17185	90	17	where	where	SCONJ
fcis-17185	90	18	1	1	NUM
fcis-17185	90	19	jx	jx	PROPN
fcis-17185	90	20	denotes	denote	VERB
fcis-17185	90	21	the	the	DET
fcis-17185	90	22	position	position	NOUN
fcis-17185	90	23	of	of	ADP
fcis-17185	90	24	the	the	DET
fcis-17185	90	25	leader	leader	NOUN
fcis-17185	90	26	in	in	ADP
fcis-17185	90	27	the	the	DET
fcis-17185	90	28	j	j	PROPN
fcis-17185	90	29	th	th	X
fcis-17185	90	30	dimensional	dimensional	ADJ
fcis-17185	90	31	space	space	NOUN
fcis-17185	90	32	and	and	CCONJ
fcis-17185	90	33	jf	jf	PROPN
fcis-17185	90	34	denotes	denote	VERB
fcis-17185	90	35	the	the	DET
fcis-17185	90	36	position	position	NOUN
fcis-17185	90	37	of	of	ADP
fcis-17185	90	38	the	the	DET
fcis-17185	90	39	food	food	NOUN
fcis-17185	90	40	source	source	NOUN
fcis-17185	90	41	in	in	ADP
fcis-17185	90	42	the	the	DET
fcis-17185	90	43	same	same	ADJ
fcis-17185	90	44	space	space	NOUN
fcis-17185	90	45	.	.	PUNCT
fcis-17185	91	1	in	in	ADP
fcis-17185	91	2	addition	addition	NOUN
fcis-17185	91	3	,	,	PUNCT
fcis-17185	91	4	jub	jub	PROPN
fcis-17185	91	5	and	and	CCONJ
fcis-17185	91	6	jlb	jlb	PROPN
fcis-17185	91	7	denote	denote	VERB
fcis-17185	91	8	the	the	DET
fcis-17185	91	9	upper	upper	ADJ
fcis-17185	91	10	and	and	CCONJ
fcis-17185	91	11	lower	low	ADJ
fcis-17185	91	12	bounds	bound	NOUN
fcis-17185	91	13	of	of	ADP
fcis-17185	91	14	the	the	DET
fcis-17185	91	15	dimensional	dimensional	ADJ
fcis-17185	91	16	space	space	NOUN
fcis-17185	91	17	,	,	PUNCT
fcis-17185	91	18	respectively	respectively	ADV
fcis-17185	91	19	.	.	PUNCT
fcis-17185	92	1	the	the	DET
fcis-17185	92	2	variables	variable	NOUN
fcis-17185	92	3	2c	2c	NOUN
fcis-17185	92	4	and	and	CCONJ
fcis-17185	92	5	3c	3c	NUM
fcis-17185	92	6	are	be	AUX
fcis-17185	92	7	random	random	ADJ
fcis-17185	92	8	numbers	number	NOUN
fcis-17185	92	9	uniformly	uniformly	ADV
fcis-17185	92	10	distributed	distribute	VERB
fcis-17185	92	11	in	in	ADP
fcis-17185	92	12	the	the	DET
fcis-17185	92	13	range	range	NOUN
fcis-17185	92	14	of	of	X
fcis-17185	92	15	0,1	0,1	NOUN
fcis-17185	92	16	.	.	PUNCT
fcis-17185	93	1	the	the	DET
fcis-17185	93	2	parameter	parameter	NOUN
fcis-17185	93	3	1c	1c	NUM
fcis-17185	93	4	in	in	ADP
fcis-17185	93	5	the	the	DET
fcis-17185	93	6	ssa	ssa	NOUN
fcis-17185	93	7	algorithm	algorithm	NOUN
fcis-17185	93	8	is	be	AUX
fcis-17185	93	9	used	use	VERB
fcis-17185	93	10	as	as	ADP
fcis-17185	93	11	a	a	DET
fcis-17185	93	12	coefficient	coefficient	NOUN
fcis-17185	93	13	to	to	PART
fcis-17185	93	14	balance	balance	VERB
fcis-17185	93	15	the	the	DET
fcis-17185	93	16	exploration	exploration	NOUN
fcis-17185	93	17	and	and	CCONJ
fcis-17185	93	18	exploitation	exploitation	NOUN
fcis-17185	93	19	,	,	PUNCT
fcis-17185	93	20	which	which	PRON
fcis-17185	93	21	is	be	AUX
fcis-17185	93	22	given	give	VERB
fcis-17185	93	23	by	by	ADP
fcis-17185	93	24	:	:	PUNCT
fcis-17185	93	25	4	4	NUM
fcis-17185	93	26	1	1	NUM
fcis-17185	93	27	2	2	NUM
fcis-17185	93	28	ml	ml	NOUN
fcis-17185	93	29	lc	lc	PROPN
fcis-17185	93	30	e	e	PROPN
fcis-17185	93	31			NOUN
fcis-17185	93	32			PROPN
fcis-17185	93	33			PROPN
fcis-17185	94	1			PROPN
fcis-17185	94	2			PROPN
fcis-17185	94	3	(	(	PUNCT
fcis-17185	94	4	7	7	NUM
fcis-17185	94	5	)	)	PUNCT
fcis-17185	94	6	where	where	SCONJ
fcis-17185	94	7	l	l	NOUN
fcis-17185	94	8	denotes	denote	VERB
fcis-17185	94	9	the	the	DET
fcis-17185	94	10	current	current	ADJ
fcis-17185	94	11	number	number	NOUN
fcis-17185	94	12	of	of	ADP
fcis-17185	94	13	iterations	iteration	NOUN
fcis-17185	94	14	and	and	CCONJ
fcis-17185	94	15	l	l	NOUN
fcis-17185	94	16	is	be	AUX
fcis-17185	94	17	the	the	DET
fcis-17185	94	18	maximum	maximum	ADJ
fcis-17185	94	19	number	number	NOUN
fcis-17185	94	20	of	of	ADP
fcis-17185	94	21	iterations	iteration	NOUN
fcis-17185	94	22	of	of	ADP
fcis-17185	94	23	the	the	DET
fcis-17185	94	24	algorithm	algorithm	NOUN
fcis-17185	94	25	.	.	PUNCT
fcis-17185	95	1	the	the	DET
fcis-17185	95	2	position	position	NOUN
fcis-17185	95	3	of	of	ADP
fcis-17185	95	4	the	the	DET
fcis-17185	95	5	follower	follower	NOUN
fcis-17185	95	6	is	be	AUX
fcis-17185	95	7	updated	update	VERB
fcis-17185	95	8	according	accord	VERB
fcis-17185	95	9	to	to	ADP
fcis-17185	95	10	newton	newton	PROPN
fcis-17185	95	11	's	's	PART
fcis-17185	95	12	law	law	NOUN
fcis-17185	95	13	of	of	ADP
fcis-17185	95	14	motion	motion	NOUN
fcis-17185	95	15	,	,	PUNCT
fcis-17185	95	16	which	which	PRON
fcis-17185	95	17	can	can	AUX
fcis-17185	95	18	be	be	AUX
fcis-17185	95	19	expressed	express	VERB
fcis-17185	95	20	as	as	ADP
fcis-17185	95	21	:	:	PUNCT
fcis-17185	95	22			NOUN
fcis-17185	95	23	11	11	NOUN
fcis-17185	95	24	2	2	NUM
fcis-17185	96	1	i	i	PRON
fcis-17185	96	2	i	i	PRON
fcis-17185	97	1	i	i	PRON
fcis-17185	97	2	j	j	PROPN
fcis-17185	98	1	j	j	NOUN
fcis-17185	98	2	jx	jx	PROPN
fcis-17185	98	3	x	x	PROPN
fcis-17185	98	4	x	x	SYM
fcis-17185	98	5			PROPN
fcis-17185	98	6			X
fcis-17185	98	7	(	(	PUNCT
fcis-17185	98	8	8)	8)	NUM
fcis-17185	98	9	66	66	NUM
fcis-17185	98	10	3	3	NUM
fcis-17185	98	11	.	.	PUNCT
fcis-17185	99	1	extreme	extreme	ADJ
fcis-17185	99	2	learning	learning	NOUN
fcis-17185	99	3	machine	machine	NOUN
fcis-17185	99	4	model	model	NOUN
fcis-17185	99	5	with	with	ADP
fcis-17185	99	6	twin	twin	ADJ
fcis-17185	99	7	strategy	strategy	NOUN
fcis-17185	99	8	salp	salp	NOUN
fcis-17185	99	9	swarm	swarm	NOUN
fcis-17185	99	10	algorithm	algorithm	NOUN
fcis-17185	99	11	(	(	PUNCT
fcis-17185	99	12	1	1	X
fcis-17185	99	13	)	)	PUNCT
fcis-17185	99	14	twin	twin	ADJ
fcis-17185	99	15	strategy	strategy	NOUN
fcis-17185	99	16	salp	salp	NOUN
fcis-17185	99	17	swarm	swarm	NOUN
fcis-17185	99	18	algorithm	algorithm	NOUN
fcis-17185	99	19	in	in	ADP
fcis-17185	99	20	the	the	DET
fcis-17185	99	21	search	search	NOUN
fcis-17185	99	22	space	space	NOUN
fcis-17185	99	23	of	of	ADP
fcis-17185	99	24	an	an	DET
fcis-17185	99	25	evolutionary	evolutionary	ADJ
fcis-17185	99	26	algorithm	algorithm	NOUN
fcis-17185	99	27	,	,	PUNCT
fcis-17185	99	28	the	the	DET
fcis-17185	99	29	initial	initial	ADJ
fcis-17185	99	30	population	population	NOUN
fcis-17185	99	31	setting	set	VERB
fcis-17185	99	32	plays	play	VERB
fcis-17185	99	33	a	a	DET
fcis-17185	99	34	crucial	crucial	ADJ
fcis-17185	99	35	role	role	NOUN
fcis-17185	99	36	in	in	ADP
fcis-17185	99	37	determining	determine	VERB
fcis-17185	99	38	the	the	DET
fcis-17185	99	39	starting	starting	NOUN
fcis-17185	99	40	point	point	NOUN
fcis-17185	99	41	and	and	CCONJ
fcis-17185	99	42	search	search	NOUN
fcis-17185	99	43	direction	direction	NOUN
fcis-17185	99	44	.	.	PUNCT
fcis-17185	100	1	different	different	ADJ
fcis-17185	100	2	initialization	initialization	NOUN
fcis-17185	100	3	methods	method	NOUN
fcis-17185	100	4	may	may	AUX
fcis-17185	100	5	lead	lead	VERB
fcis-17185	100	6	to	to	ADP
fcis-17185	100	7	different	different	ADJ
fcis-17185	100	8	search	search	NOUN
fcis-17185	100	9	results	result	NOUN
fcis-17185	100	10	for	for	ADP
fcis-17185	100	11	the	the	DET
fcis-17185	100	12	algorithm	algorithm	NOUN
fcis-17185	100	13	and	and	CCONJ
fcis-17185	100	14	may	may	AUX
fcis-17185	100	15	even	even	ADV
fcis-17185	100	16	affect	affect	VERB
fcis-17185	100	17	its	its	PRON
fcis-17185	100	18	ability	ability	NOUN
fcis-17185	100	19	to	to	PART
fcis-17185	100	20	discover	discover	VERB
fcis-17185	100	21	globally	globally	ADV
fcis-17185	100	22	optimal	optimal	ADJ
fcis-17185	100	23	solutions	solution	NOUN
fcis-17185	100	24	.	.	PUNCT
fcis-17185	101	1	inappropriate	inappropriate	ADJ
fcis-17185	101	2	population	population	NOUN
fcis-17185	101	3	initialization	initialization	NOUN
fcis-17185	101	4	may	may	AUX
fcis-17185	101	5	cause	cause	VERB
fcis-17185	101	6	the	the	DET
fcis-17185	101	7	algorithm	algorithm	NOUN
fcis-17185	101	8	to	to	PART
fcis-17185	101	9	converge	converge	VERB
fcis-17185	101	10	prematurely	prematurely	ADV
fcis-17185	101	11	,	,	PUNCT
fcis-17185	101	12	hindering	hinder	VERB
fcis-17185	101	13	its	its	PRON
fcis-17185	101	14	ability	ability	NOUN
fcis-17185	101	15	to	to	PART
fcis-17185	101	16	explore	explore	VERB
fcis-17185	101	17	better	well	ADJ
fcis-17185	101	18	solutions	solution	NOUN
fcis-17185	101	19	.	.	PUNCT
fcis-17185	102	1	conversely	conversely	ADV
fcis-17185	102	2	,	,	PUNCT
fcis-17185	102	3	overly	overly	ADV
fcis-17185	102	4	random	random	ADJ
fcis-17185	102	5	initialization	initialization	NOUN
fcis-17185	102	6	can	can	AUX
fcis-17185	102	7	lead	lead	VERB
fcis-17185	102	8	to	to	ADP
fcis-17185	102	9	a	a	DET
fcis-17185	102	10	large	large	ADJ
fcis-17185	102	11	number	number	NOUN
fcis-17185	102	12	of	of	ADP
fcis-17185	102	13	low	low	ADJ
fcis-17185	102	14	-	-	PUNCT
fcis-17185	102	15	quality	quality	NOUN
fcis-17185	102	16	individuals	individual	NOUN
fcis-17185	102	17	in	in	ADP
fcis-17185	102	18	the	the	DET
fcis-17185	102	19	population	population	NOUN
fcis-17185	102	20	.	.	PUNCT
fcis-17185	103	1	this	this	PRON
fcis-17185	103	2	will	will	AUX
fcis-17185	103	3	inevitably	inevitably	ADV
fcis-17185	103	4	reduce	reduce	VERB
fcis-17185	103	5	the	the	DET
fcis-17185	103	6	search	search	NOUN
fcis-17185	103	7	efficiency	efficiency	NOUN
fcis-17185	103	8	of	of	ADP
fcis-17185	103	9	the	the	DET
fcis-17185	103	10	algorithm	algorithm	NOUN
fcis-17185	103	11	and	and	CCONJ
fcis-17185	103	12	require	require	VERB
fcis-17185	103	13	more	more	ADJ
fcis-17185	103	14	time	time	NOUN
fcis-17185	103	15	to	to	PART
fcis-17185	103	16	reach	reach	VERB
fcis-17185	103	17	the	the	DET
fcis-17185	103	18	optimal	optimal	ADJ
fcis-17185	103	19	solution	solution	NOUN
fcis-17185	103	20	.	.	PUNCT
fcis-17185	104	1	in	in	ADP
fcis-17185	104	2	order	order	NOUN
fcis-17185	104	3	to	to	PART
fcis-17185	104	4	improve	improve	VERB
fcis-17185	104	5	the	the	DET
fcis-17185	104	6	training	training	NOUN
fcis-17185	104	7	effect	effect	NOUN
fcis-17185	104	8	and	and	CCONJ
fcis-17185	104	9	generalization	generalization	NOUN
fcis-17185	104	10	ability	ability	NOUN
fcis-17185	104	11	of	of	ADP
fcis-17185	104	12	the	the	DET
fcis-17185	104	13	model	model	NOUN
fcis-17185	104	14	,	,	PUNCT
fcis-17185	104	15	this	this	DET
fcis-17185	104	16	paper	paper	NOUN
fcis-17185	104	17	uses	use	VERB
fcis-17185	104	18	a	a	DET
fcis-17185	104	19	chaotic	chaotic	ADJ
fcis-17185	104	20	approach	approach	NOUN
fcis-17185	104	21	to	to	PART
fcis-17185	104	22	initialize	initialize	VERB
fcis-17185	104	23	the	the	DET
fcis-17185	104	24	population	population	NOUN
fcis-17185	104	25	.	.	PUNCT
fcis-17185	105	1	chaotic	chaotic	ADJ
fcis-17185	105	2	random	random	ADJ
fcis-17185	105	3	numbers	number	NOUN
fcis-17185	105	4	are	be	AUX
fcis-17185	105	5	highly	highly	ADV
fcis-17185	105	6	sensitive	sensitive	ADJ
fcis-17185	105	7	and	and	CCONJ
fcis-17185	105	8	nonlinear	nonlinear	ADJ
fcis-17185	105	9	,	,	PUNCT
fcis-17185	105	10	so	so	ADV
fcis-17185	105	11	much	much	ADV
fcis-17185	105	12	so	so	ADV
fcis-17185	105	13	that	that	SCONJ
fcis-17185	105	14	slight	slight	ADJ
fcis-17185	105	15	perturbations	perturbation	NOUN
fcis-17185	105	16	in	in	ADP
fcis-17185	105	17	the	the	DET
fcis-17185	105	18	initial	initial	ADJ
fcis-17185	105	19	conditions	condition	NOUN
fcis-17185	105	20	or	or	CCONJ
fcis-17185	105	21	parameters	parameter	NOUN
fcis-17185	105	22	may	may	AUX
fcis-17185	105	23	lead	lead	VERB
fcis-17185	105	24	to	to	ADP
fcis-17185	105	25	significantly	significantly	ADV
fcis-17185	105	26	different	different	ADJ
fcis-17185	105	27	trajectories	trajectory	NOUN
fcis-17185	105	28	of	of	ADP
fcis-17185	105	29	the	the	DET
fcis-17185	105	30	system	system	NOUN
fcis-17185	105	31	,	,	PUNCT
fcis-17185	105	32	exhibiting	exhibit	VERB
fcis-17185	105	33	robust	robust	ADJ
fcis-17185	105	34	statistical	statistical	ADJ
fcis-17185	105	35	randomness	randomness	NOUN
fcis-17185	105	36	properties	property	NOUN
fcis-17185	105	37	.	.	PUNCT
fcis-17185	106	1	specifically	specifically	ADV
fcis-17185	106	2	,	,	PUNCT
fcis-17185	106	3	we	we	PRON
fcis-17185	106	4	use	use	VERB
fcis-17185	106	5	cubic	cubic	ADJ
fcis-17185	106	6	chaotic	chaotic	ADJ
fcis-17185	106	7	mapping	mapping	NOUN
fcis-17185	106	8	instead	instead	ADV
fcis-17185	106	9	of	of	ADP
fcis-17185	106	10	random	random	ADJ
fcis-17185	106	11	initialization	initialization	NOUN
fcis-17185	106	12	,	,	PUNCT
fcis-17185	106	13	which	which	PRON
fcis-17185	106	14	is	be	AUX
fcis-17185	106	15	defined	define	VERB
fcis-17185	106	16	by	by	ADP
fcis-17185	106	17	the	the	DET
fcis-17185	106	18	following	follow	VERB
fcis-17185	106	19	formula	formula	NOUN
fcis-17185	106	20	.	.	PUNCT
fcis-17185	107	1			NOUN
fcis-17185	107	2	2	2	ADJ
fcis-17185	107	3	1	1	NUM
fcis-17185	107	4	1n	1n	NUM
fcis-17185	107	5	n	n	CCONJ
fcis-17185	107	6	nch	nch	PROPN
fcis-17185	107	7	ch	ch	PROPN
fcis-17185	107	8	ch	ch	VERB
fcis-17185	108	1			PROPN
fcis-17185	108	2			PROPN
fcis-17185	108	3	(	(	PUNCT
fcis-17185	108	4	9	9	NUM
fcis-17185	108	5	)	)	PUNCT
fcis-17185	108	6			NOUN
fcis-17185	108	7			PROPN
fcis-17185	108	8	,	,	PUNCT
fcis-17185	108	9	min	min	PROPN
fcis-17185	108	10	,	,	PUNCT
fcis-17185	108	11	,	,	PUNCT
fcis-17185	108	12	max	max	PROPN
fcis-17185	108	13	,	,	PUNCT
fcis-17185	108	14	min	min	PROPN
fcis-17185	108	15	,	,	PUNCT
fcis-17185	108	16	i	i	PRON
fcis-17185	108	17	j	j	PROPN
fcis-17185	109	1	j	j	NOUN
fcis-17185	110	1	i	i	PRON
fcis-17185	110	2	j	j	PROPN
fcis-17185	111	1	j	j	PROPN
fcis-17185	111	2	jx	jx	PROPN
fcis-17185	112	1	x	x	PROPN
fcis-17185	112	2	ch	ch	NOUN
fcis-17185	112	3	x	x	X
fcis-17185	113	1	x	x	PROPN
fcis-17185	113	2			PUNCT
fcis-17185	113	3			X
fcis-17185	113	4	(	(	PUNCT
fcis-17185	113	5	10	10	NUM
fcis-17185	113	6	)	)	PUNCT
fcis-17185	113	7	where	where	SCONJ
fcis-17185	113	8	the	the	DET
fcis-17185	113	9	cubic	cubic	ADJ
fcis-17185	113	10	chaotic	chaotic	ADJ
fcis-17185	113	11	mapping	mapping	NOUN
fcis-17185	113	12	has	have	VERB
fcis-17185	113	13	good	good	ADJ
fcis-17185	113	14	chaotic	chaotic	ADJ
fcis-17185	113	15	traversal	traversal	NOUN
fcis-17185	113	16	properties	property	NOUN
fcis-17185	113	17	when	when	SCONJ
fcis-17185	113	18	0	0	NUM
fcis-17185	114	1	0.3ch	0.3ch	NUM
fcis-17185	114	2			NUM
fcis-17185	114	3	and	and	CCONJ
fcis-17185	114	4	2.595	2.595	NUM
fcis-17185	114	5			NUM
fcis-17185	114	6	.	.	PUNCT
fcis-17185	115	1	the	the	DET
fcis-17185	115	2	position	position	NOUN
fcis-17185	115	3	update	update	NOUN
fcis-17185	115	4	of	of	ADP
fcis-17185	115	5	a	a	DET
fcis-17185	115	6	follower	follower	NOUN
fcis-17185	115	7	in	in	ADP
fcis-17185	115	8	classical	classical	ADJ
fcis-17185	115	9	ssa	ssa	NOUN
fcis-17185	115	10	is	be	AUX
fcis-17185	115	11	related	relate	VERB
fcis-17185	115	12	to	to	ADP
fcis-17185	115	13	the	the	DET
fcis-17185	115	14	position	position	NOUN
fcis-17185	115	15	information	information	NOUN
fcis-17185	115	16	of	of	ADP
fcis-17185	115	17	its	its	PRON
fcis-17185	115	18	previous	previous	ADJ
fcis-17185	115	19	individual	individual	NOUN
fcis-17185	115	20	,	,	PUNCT
fcis-17185	115	21	but	but	CCONJ
fcis-17185	115	22	it	it	PRON
fcis-17185	115	23	is	be	AUX
fcis-17185	115	24	a	a	DET
fcis-17185	115	25	purely	purely	ADV
fcis-17185	115	26	blind	blind	ADJ
fcis-17185	115	27	follower	follower	NOUN
fcis-17185	115	28	behavior	behavior	NOUN
fcis-17185	115	29	,	,	PUNCT
fcis-17185	115	30	which	which	PRON
fcis-17185	115	31	updates	update	VERB
fcis-17185	115	32	in	in	ADP
fcis-17185	115	33	a	a	DET
fcis-17185	115	34	single	single	ADJ
fcis-17185	115	35	way	way	NOUN
fcis-17185	115	36	and	and	CCONJ
fcis-17185	115	37	limits	limit	VERB
fcis-17185	115	38	the	the	DET
fcis-17185	115	39	search	search	NOUN
fcis-17185	115	40	scope	scope	NOUN
fcis-17185	115	41	.	.	PUNCT
fcis-17185	116	1	in	in	ADP
fcis-17185	116	2	order	order	NOUN
fcis-17185	116	3	to	to	PART
fcis-17185	116	4	quickly	quickly	ADV
fcis-17185	116	5	traverse	traverse	VERB
fcis-17185	116	6	the	the	DET
fcis-17185	116	7	search	search	NOUN
fcis-17185	116	8	space	space	NOUN
fcis-17185	116	9	for	for	ADP
fcis-17185	116	10	global	global	ADJ
fcis-17185	116	11	search	search	NOUN
fcis-17185	116	12	this	this	DET
fcis-17185	116	13	paper	paper	NOUN
fcis-17185	116	14	adopts	adopt	VERB
fcis-17185	116	15	a	a	DET
fcis-17185	116	16	twin	twin	ADJ
fcis-17185	116	17	strategy	strategy	NOUN
fcis-17185	116	18	follower	follower	NOUN
fcis-17185	116	19	update	update	NOUN
fcis-17185	116	20	mechanism	mechanism	NOUN
fcis-17185	116	21	.	.	PUNCT
fcis-17185	117	1	strategy	strategy	NOUN
fcis-17185	117	2	1	1	NUM
fcis-17185	117	3	:	:	PUNCT
fcis-17185	117	4	introduce	introduce	VERB
fcis-17185	117	5	the	the	DET
fcis-17185	117	6	oscillatory	oscillatory	ADJ
fcis-17185	117	7	inertia	inertia	NOUN
fcis-17185	117	8	weight	weight	NOUN
fcis-17185	117	9	w	w	NOUN
fcis-17185	117	10	to	to	PART
fcis-17185	117	11	update	update	VERB
fcis-17185	117	12	the	the	DET
fcis-17185	117	13	follower	follower	NOUN
fcis-17185	117	14	position	position	NOUN
fcis-17185	117	15	information	information	NOUN
fcis-17185	117	16	,	,	PUNCT
fcis-17185	117	17	the	the	DET
fcis-17185	117	18	oscillatory	oscillatory	ADJ
fcis-17185	117	19	inertia	inertia	NOUN
fcis-17185	117	20	weight	weight	NOUN
fcis-17185	117	21	performs	perform	VERB
fcis-17185	117	22	global	global	ADJ
fcis-17185	117	23	exploration	exploration	NOUN
fcis-17185	117	24	in	in	ADP
fcis-17185	117	25	the	the	DET
fcis-17185	117	26	preevolutionary	preevolutionary	ADJ
fcis-17185	117	27	stage	stage	NOUN
fcis-17185	117	28	,	,	PUNCT
fcis-17185	117	29	and	and	CCONJ
fcis-17185	117	30	the	the	DET
fcis-17185	117	31	movement	movement	NOUN
fcis-17185	117	32	decreases	decrease	VERB
fcis-17185	117	33	in	in	ADP
fcis-17185	117	34	the	the	DET
fcis-17185	117	35	late	late	ADJ
fcis-17185	117	36	evolutionary	evolutionary	ADJ
fcis-17185	117	37	stage	stage	NOUN
fcis-17185	117	38	,	,	PUNCT
fcis-17185	117	39	focusing	focus	VERB
fcis-17185	117	40	on	on	ADP
fcis-17185	117	41	the	the	DET
fcis-17185	117	42	local	local	ADJ
fcis-17185	117	43	exploitation	exploitation	NOUN
fcis-17185	117	44	to	to	PART
fcis-17185	117	45	be	be	AUX
fcis-17185	117	46	able	able	ADJ
fcis-17185	117	47	to	to	PART
fcis-17185	117	48	excavate	excavate	VERB
fcis-17185	117	49	the	the	DET
fcis-17185	117	50	optimal	optimal	ADJ
fcis-17185	117	51	solution	solution	NOUN
fcis-17185	117	52	more	more	ADV
fcis-17185	117	53	accurately	accurately	ADV
fcis-17185	117	54	,	,	PUNCT
fcis-17185	117	55	so	so	SCONJ
fcis-17185	117	56	as	as	SCONJ
fcis-17185	117	57	to	to	PART
fcis-17185	117	58	be	be	AUX
fcis-17185	117	59	able	able	ADJ
fcis-17185	117	60	to	to	PART
fcis-17185	117	61	balance	balance	VERB
fcis-17185	117	62	the	the	DET
fcis-17185	117	63	exploitation	exploitation	NOUN
fcis-17185	117	64	and	and	CCONJ
fcis-17185	117	65	exploration	exploration	NOUN
fcis-17185	117	66	line	line	NOUN
fcis-17185	117	67	ability	ability	NOUN
fcis-17185	117	68	during	during	ADP
fcis-17185	117	69	the	the	DET
fcis-17185	117	70	global	global	ADJ
fcis-17185	117	71	search	search	NOUN
fcis-17185	117	72	.	.	PUNCT
fcis-17185	118	1	at	at	ADP
fcis-17185	118	2	this	this	DET
fcis-17185	118	3	time	time	NOUN
fcis-17185	118	4	,	,	PUNCT
fcis-17185	118	5	the	the	DET
fcis-17185	118	6	follower	follower	NOUN
fcis-17185	118	7	's	's	PART
fcis-17185	118	8	position	position	NOUN
fcis-17185	118	9	update	update	NOUN
fcis-17185	118	10	formula	formula	NOUN
fcis-17185	118	11	is	be	AUX
fcis-17185	118	12	as	as	SCONJ
fcis-17185	118	13	follows	follow	VERB
fcis-17185	118	14	:	:	PUNCT
fcis-17185	119	1			NOUN
fcis-17185	119	2	0.5	0.5	VERB
fcis-17185	119	3	2	2	NUM
fcis-17185	119	4	tan	tan	NOUN
fcis-17185	119	5	(	(	PUNCT
fcis-17185	119	6	)	)	PUNCT
fcis-17185	119	7	l	l	NOUN
fcis-17185	119	8	w	w	NOUN
fcis-17185	119	9	rand	rand	PROPN
fcis-17185	119	10	l	l	PROPN
fcis-17185	119	11			PROPN
fcis-17185	119	12			PROPN
fcis-17185	120	1			PROPN
fcis-17185	120	2			PROPN
fcis-17185	120	3			CCONJ
fcis-17185	120	4			PROPN
fcis-17185	120	5			NOUN
fcis-17185	120	6	(	(	PUNCT
fcis-17185	120	7	11	11	NUM
fcis-17185	120	8	)	)	PUNCT
fcis-17185	120	9			NOUN
fcis-17185	120	10	11	11	PUNCT
fcis-17185	120	11	2	2	NUM
fcis-17185	121	1	i	i	PRON
fcis-17185	121	2	i	i	PRON
fcis-17185	122	1	i	i	PRON
fcis-17185	122	2	j	j	PROPN
fcis-17185	123	1	j	j	PROPN
fcis-17185	123	2	jx	jx	PROPN
fcis-17185	123	3	x	x	PROPN
fcis-17185	123	4	wx	wx	PROPN
fcis-17185	123	5			PROPN
fcis-17185	123	6			X
fcis-17185	123	7	(	(	PUNCT
fcis-17185	123	8	12	12	NUM
fcis-17185	123	9	)	)	PUNCT
fcis-17185	123	10	where	where	SCONJ
fcis-17185	123	11	rand	rand	NOUN
fcis-17185	123	12	is	be	AUX
fcis-17185	123	13	a	a	DET
fcis-17185	123	14	uniformly	uniformly	ADV
fcis-17185	123	15	distributed	distribute	VERB
fcis-17185	123	16	random	random	ADJ
fcis-17185	123	17	number	number	NOUN
fcis-17185	123	18	between	between	ADP
fcis-17185	123	19			NOUN
fcis-17185	123	20	0,1	0,1	NOUN
fcis-17185	123	21	.	.	PUNCT
fcis-17185	124	1	strategy	strategy	NOUN
fcis-17185	124	2	2	2	NUM
fcis-17185	124	3	:	:	PUNCT
fcis-17185	124	4	according	accord	VERB
fcis-17185	124	5	to	to	ADP
fcis-17185	124	6	the	the	DET
fcis-17185	124	7	fitness	fitness	NOUN
fcis-17185	124	8	information	information	NOUN
fcis-17185	124	9	of	of	ADP
fcis-17185	124	10	the	the	DET
fcis-17185	124	11	population	population	NOUN
fcis-17185	124	12	in	in	ADP
fcis-17185	124	13	the	the	DET
fcis-17185	124	14	solution	solution	NOUN
fcis-17185	124	15	space	space	NOUN
fcis-17185	124	16	,	,	PUNCT
fcis-17185	124	17	sort	sort	ADV
fcis-17185	124	18	all	all	DET
fcis-17185	124	19	the	the	DET
fcis-17185	124	20	individuals	individual	NOUN
fcis-17185	124	21	of	of	ADP
fcis-17185	124	22	the	the	DET
fcis-17185	124	23	population	population	NOUN
fcis-17185	124	24	,	,	PUNCT
fcis-17185	124	25	and	and	CCONJ
fcis-17185	124	26	get	get	VERB
fcis-17185	124	27	the	the	DET
fcis-17185	124	28	sequence	sequence	NOUN
fcis-17185	124	29	information	information	NOUN
fcis-17185	124	30			NOUN
fcis-17185	125	1	rank	rank	PROPN
fcis-17185	126	1	i	i	PRON
fcis-17185	126	2	of	of	ADP
fcis-17185	126	3	each	each	DET
fcis-17185	126	4	individual	individual	NOUN
fcis-17185	126	5	,	,	PUNCT
fcis-17185	126	6	according	accord	VERB
fcis-17185	126	7	to	to	ADP
fcis-17185	126	8	the	the	DET
fcis-17185	126	9	sorting	sort	VERB
fcis-17185	126	10	result	result	NOUN
fcis-17185	126	11	,	,	PUNCT
fcis-17185	126	12	we	we	PRON
fcis-17185	126	13	select	select	VERB
fcis-17185	126	14	the	the	DET
fcis-17185	126	15	individual	individual	NOUN
fcis-17185	126	16	with	with	ADP
fcis-17185	126	17	better	well	ADJ
fcis-17185	126	18	fitness	fitness	NOUN
fcis-17185	126	19	information	information	NOUN
fcis-17185	126	20	as	as	ADP
fcis-17185	126	21	the	the	DET
fcis-17185	126	22	learned	learn	VERB
fcis-17185	126	23	example	example	NOUN
fcis-17185	126	24	,	,	PUNCT
fcis-17185	126	25	in	in	ADP
fcis-17185	126	26	order	order	NOUN
fcis-17185	126	27	to	to	PART
fcis-17185	126	28	avoid	avoid	VERB
fcis-17185	126	29	arbitrary	arbitrary	ADJ
fcis-17185	126	30	selection	selection	NOUN
fcis-17185	126	31	of	of	ADP
fcis-17185	126	32	the	the	DET
fcis-17185	126	33	learned	learn	VERB
fcis-17185	126	34	example	example	NOUN
fcis-17185	126	35	,	,	PUNCT
fcis-17185	126	36	we	we	PRON
fcis-17185	126	37	use	use	VERB
fcis-17185	126	38	probabilistic	probabilistic	ADJ
fcis-17185	126	39	way	way	NOUN
fcis-17185	126	40	to	to	PART
fcis-17185	126	41	select	select	VERB
fcis-17185	126	42	.	.	PUNCT
fcis-17185	127	1	when	when	SCONJ
fcis-17185	127	2			PROPN
fcis-17185	127	3	rank	rank	PROPN
fcis-17185	127	4	i	i	PRON
fcis-17185	127	5	rand	rand	VERB
fcis-17185	127	6	p	p	VERB
fcis-17185	127	7			NUM
fcis-17185	127	8	is	be	AUX
fcis-17185	127	9	satisfied	satisfied	ADJ
fcis-17185	127	10	,	,	PUNCT
fcis-17185	127	11	rand	rand	NOUN
fcis-17185	127	12	is	be	AUX
fcis-17185	127	13	a	a	DET
fcis-17185	127	14	random	random	ADJ
fcis-17185	127	15	number	number	NOUN
fcis-17185	127	16	between	between	ADP
fcis-17185	127	17			NOUN
fcis-17185	127	18	0,1	0,1	NOUN
fcis-17185	127	19	,	,	PUNCT
fcis-17185	127	20	which	which	PRON
fcis-17185	127	21	is	be	AUX
fcis-17185	127	22	put	put	VERB
fcis-17185	127	23	into	into	ADP
fcis-17185	127	24	the	the	DET
fcis-17185	127	25	set	set	NOUN
fcis-17185	127	26	q	q	NOUN
fcis-17185	127	27	of	of	ADP
fcis-17185	127	28	learned	learn	VERB
fcis-17185	127	29	examples	example	NOUN
fcis-17185	127	30	.	.	PUNCT
fcis-17185	128	1	this	this	PRON
fcis-17185	128	2	means	mean	VERB
fcis-17185	128	3	that	that	SCONJ
fcis-17185	128	4	when	when	SCONJ
fcis-17185	128	5	the	the	DET
fcis-17185	128	6	individual	individual	NOUN
fcis-17185	128	7	's	's	PART
fcis-17185	128	8	fitness	fitness	NOUN
fcis-17185	128	9	information	information	NOUN
fcis-17185	128	10	is	be	AUX
fcis-17185	128	11	better	well	ADJ
fcis-17185	128	12	,	,	PUNCT
fcis-17185	128	13	the	the	DET
fcis-17185	128	14	individual	individual	NOUN
fcis-17185	128	15	's	's	PART
fcis-17185	128	16	ranking	ranking	NOUN
fcis-17185	128	17	is	be	AUX
fcis-17185	128	18	more	more	ADV
fcis-17185	128	19	advanced	advanced	ADJ
fcis-17185	128	20	,	,	PUNCT
fcis-17185	128	21	and	and	CCONJ
fcis-17185	128	22	then	then	ADV
fcis-17185	128	23	there	there	PRON
fcis-17185	128	24	is	be	VERB
fcis-17185	128	25	a	a	DET
fcis-17185	128	26	greater	great	ADJ
fcis-17185	128	27	possibility	possibility	NOUN
fcis-17185	128	28	to	to	PART
fcis-17185	128	29	satisfy	satisfy	VERB
fcis-17185	128	30	the	the	DET
fcis-17185	128	31	probabilistic	probabilistic	ADJ
fcis-17185	128	32	selection	selection	NOUN
fcis-17185	128	33	formula	formula	NOUN
fcis-17185	128	34	we	we	PRON
fcis-17185	128	35	set	set	VERB
fcis-17185	128	36	.	.	PUNCT
fcis-17185	129	1	the	the	DET
fcis-17185	129	2	follower	follower	NOUN
fcis-17185	129	3	's	's	PART
fcis-17185	129	4	position	position	NOUN
fcis-17185	129	5	update	update	NOUN
fcis-17185	129	6	formula	formula	NOUN
fcis-17185	129	7	is	be	AUX
fcis-17185	129	8	as	as	SCONJ
fcis-17185	129	9	follows	follow	VERB
fcis-17185	129	10	:	:	PUNCT
fcis-17185	129	11			NOUN
fcis-17185	129	12	1	1	VERB
fcis-17185	129	13	2	2	NUM
fcis-17185	130	1	i	i	PRON
fcis-17185	130	2	i	i	PRON
fcis-17185	131	1	i	i	PRON
fcis-17185	131	2	j	j	PROPN
fcis-17185	132	1	j	j	PROPN
fcis-17185	132	2	jx	jx	PROPN
fcis-17185	132	3	x	x	PROPN
fcis-17185	133	1	q	q	PROPN
fcis-17185	133	2			X
fcis-17185	133	3	(	(	PUNCT
fcis-17185	133	4	13	13	NUM
fcis-17185	133	5	)	)	PUNCT
fcis-17185	133	6	where	where	SCONJ
fcis-17185	133	7	i	i	PRON
fcis-17185	133	8	jq	jq	PROPN
fcis-17185	133	9	is	be	AUX
fcis-17185	133	10	the	the	DET
fcis-17185	133	11	j	j	PROPN
fcis-17185	133	12	th	th	X
fcis-17185	133	13	dimension	dimension	NOUN
fcis-17185	133	14	of	of	ADP
fcis-17185	133	15	the	the	PRON
fcis-17185	133	16	i	i	PRON
fcis-17185	133	17	th	th	X
fcis-17185	133	18	randomly	randomly	ADV
fcis-17185	133	19	selected	select	VERB
fcis-17185	133	20	individual	individual	NOUN
fcis-17185	133	21	in	in	ADP
fcis-17185	133	22	the	the	DET
fcis-17185	133	23	setq	setq	NOUN
fcis-17185	133	24	of	of	ADP
fcis-17185	133	25	learning	learn	VERB
fcis-17185	133	26	examples	example	NOUN
fcis-17185	133	27	.	.	PUNCT
fcis-17185	134	1	in	in	ADP
fcis-17185	134	2	order	order	NOUN
fcis-17185	134	3	to	to	PART
fcis-17185	134	4	ensure	ensure	VERB
fcis-17185	134	5	that	that	SCONJ
fcis-17185	134	6	the	the	DET
fcis-17185	134	7	population	population	NOUN
fcis-17185	134	8	evolves	evolve	VERB
fcis-17185	134	9	in	in	ADP
fcis-17185	134	10	a	a	DET
fcis-17185	134	11	better	well	ADJ
fcis-17185	134	12	direction	direction	NOUN
fcis-17185	134	13	,	,	PUNCT
fcis-17185	134	14	we	we	PRON
fcis-17185	134	15	adopt	adopt	VERB
fcis-17185	134	16	a	a	DET
fcis-17185	134	17	pairwise	pairwise	NOUN
fcis-17185	134	18	comparison	comparison	NOUN
fcis-17185	134	19	mechanism	mechanism	NOUN
fcis-17185	134	20	to	to	PART
fcis-17185	134	21	select	select	VERB
fcis-17185	134	22	better	well	ADV
fcis-17185	134	23	adapted	adapt	VERB
fcis-17185	134	24	individuals	individual	NOUN
fcis-17185	134	25	as	as	ADP
fcis-17185	134	26	the	the	DET
fcis-17185	134	27	offspring	offspring	NOUN
fcis-17185	134	28	of	of	ADP
fcis-17185	134	29	individuals	individual	NOUN
fcis-17185	134	30	in	in	ADP
fcis-17185	134	31	the	the	DET
fcis-17185	134	32	current	current	ADJ
fcis-17185	134	33	population	population	NOUN
fcis-17185	134	34	.	.	PUNCT
fcis-17185	135	1	algorithm	algorithm	NOUN
fcis-17185	135	2	optimization	optimization	NOUN
fcis-17185	135	3	step	step	NOUN
fcis-17185	135	4	(	(	PUNCT
fcis-17185	135	5	2	2	NUM
fcis-17185	135	6	)	)	PUNCT
fcis-17185	135	7	algorithm	algorithm	NOUN
fcis-17185	135	8	optimization	optimization	NOUN
fcis-17185	135	9	step	step	VERB
fcis-17185	135	10	the	the	DET
fcis-17185	135	11	twin	twin	ADJ
fcis-17185	135	12	strategy	strategy	NOUN
fcis-17185	135	13	salp	salp	NOUN
fcis-17185	135	14	swarm	swarm	NOUN
fcis-17185	135	15	algorithm	algorithm	NOUN
fcis-17185	135	16	is	be	AUX
fcis-17185	135	17	illustrated	illustrate	VERB
fcis-17185	135	18	in	in	ADP
fcis-17185	135	19	figure	figure	NOUN
fcis-17185	135	20	2	2	NUM
fcis-17185	135	21	.	.	PUNCT
fcis-17185	135	22	figure	figure	NOUN
fcis-17185	135	23	2	2	NUM
fcis-17185	135	24	.	.	PUNCT
fcis-17185	136	1	the	the	DET
fcis-17185	136	2	flowchart	flowchart	NOUN
fcis-17185	136	3	of	of	ADP
fcis-17185	136	4	the	the	DET
fcis-17185	136	5	twin	twin	ADJ
fcis-17185	136	6	strategy	strategy	NOUN
fcis-17185	136	7	salp	salp	NOUN
fcis-17185	136	8	swarm	swarm	NOUN
fcis-17185	136	9	algorithm	algorithm	NOUN
fcis-17185	136	10	.	.	PUNCT
fcis-17185	137	1	4	4	X
fcis-17185	137	2	.	.	X
fcis-17185	137	3	the	the	DET
fcis-17185	137	4	extreme	extreme	ADJ
fcis-17185	137	5	learning	learning	NOUN
fcis-17185	137	6	machine	machine	NOUN
fcis-17185	137	7	model	model	NOUN
fcis-17185	137	8	based	base	VERB
fcis-17185	137	9	on	on	ADP
fcis-17185	137	10	the	the	DET
fcis-17185	137	11	twin	twin	ADJ
fcis-17185	137	12	strategy	strategy	NOUN
fcis-17185	137	13	salp	salp	NOUN
fcis-17185	137	14	swarm	swarm	NOUN
fcis-17185	137	15	algorithm	algorithm	NOUN
fcis-17185	137	16	since	since	SCONJ
fcis-17185	137	17	elm	elm	NOUN
fcis-17185	137	18	randomly	randomly	ADV
fcis-17185	137	19	generates	generate	VERB
fcis-17185	137	20	the	the	DET
fcis-17185	137	21	weight	weight	NOUN
fcis-17185	137	22	matrices	matrix	NOUN
fcis-17185	137	23	connecting	connect	VERB
fcis-17185	137	24	the	the	DET
fcis-17185	137	25	input	input	NOUN
fcis-17185	137	26	and	and	CCONJ
fcis-17185	137	27	hidden	hidden	ADJ
fcis-17185	137	28	layers	layer	NOUN
fcis-17185	137	29	as	as	ADV
fcis-17185	137	30	well	well	ADV
fcis-17185	137	31	as	as	ADP
fcis-17185	137	32	the	the	DET
fcis-17185	137	33	thresholds	threshold	NOUN
fcis-17185	137	34	for	for	ADP
fcis-17185	137	35	the	the	DET
fcis-17185	137	36	hidden	hidden	ADJ
fcis-17185	137	37	layers	layer	NOUN
fcis-17185	137	38	,	,	PUNCT
fcis-17185	137	39	inappropriate	inappropriate	ADJ
fcis-17185	137	40	connection	connection	NOUN
fcis-17185	137	41	weights	weight	NOUN
fcis-17185	137	42	and	and	CCONJ
fcis-17185	137	43	thresholds	threshold	NOUN
fcis-17185	137	44	may	may	AUX
fcis-17185	137	45	lead	lead	VERB
fcis-17185	137	46	to	to	ADP
fcis-17185	137	47	invalid	invalid	ADJ
fcis-17185	137	48	nodes	node	NOUN
fcis-17185	137	49	,	,	PUNCT
fcis-17185	137	50	node	node	ADJ
fcis-17185	137	51	redundancy	redundancy	NOUN
fcis-17185	137	52	,	,	PUNCT
fcis-17185	137	53	and	and	CCONJ
fcis-17185	137	54	insufficient	insufficient	ADJ
fcis-17185	137	55	generalization	generalization	NOUN
fcis-17185	137	56	ability	ability	NOUN
fcis-17185	137	57	in	in	ADP
fcis-17185	137	58	some	some	DET
fcis-17185	137	59	hidden	hidden	ADJ
fcis-17185	137	60	layers	layer	NOUN
fcis-17185	137	61	.	.	PUNCT
fcis-17185	138	1	therefore	therefore	ADV
fcis-17185	138	2	,	,	PUNCT
fcis-17185	138	3	in	in	ADP
fcis-17185	138	4	this	this	DET
fcis-17185	138	5	paper	paper	NOUN
fcis-17185	138	6	,	,	PUNCT
fcis-17185	138	7	the	the	DET
fcis-17185	138	8	tssa	tssa	NOUN
fcis-17185	138	9	algorithm	algorithm	NOUN
fcis-17185	138	10	is	be	AUX
fcis-17185	138	11	used	use	VERB
fcis-17185	138	12	to	to	PART
fcis-17185	138	13	optimize	optimize	VERB
fcis-17185	138	14	the	the	DET
fcis-17185	138	15	connection	connection	NOUN
fcis-17185	138	16	weights	weight	NOUN
fcis-17185	138	17	and	and	CCONJ
fcis-17185	138	18	biases	bias	NOUN
fcis-17185	138	19	of	of	ADP
fcis-17185	138	20	elm	elm	PROPN
fcis-17185	138	21	.	.	PUNCT
fcis-17185	139	1	the	the	DET
fcis-17185	139	2	specific	specific	ADJ
fcis-17185	139	3	steps	step	NOUN
fcis-17185	139	4	are	be	AUX
fcis-17185	139	5	as	as	SCONJ
fcis-17185	139	6	follows	follow	VERB
fcis-17185	139	7	:	:	PUNCT
fcis-17185	139	8	step	step	NOUN
fcis-17185	139	9	1	1	NUM
fcis-17185	139	10	:	:	PUNCT
fcis-17185	139	11	construct	construct	VERB
fcis-17185	139	12	the	the	DET
fcis-17185	139	13	elm	elm	PROPN
fcis-17185	139	14	model	model	NOUN
fcis-17185	139	15	,	,	PUNCT
fcis-17185	139	16	define	define	VERB
fcis-17185	139	17	the	the	DET
fcis-17185	139	18	tssa	tssa	NOUN
fcis-17185	139	19	algorithm	algorithm	NOUN
fcis-17185	139	20	and	and	CCONJ
fcis-17185	139	21	elm	elm	NOUN
fcis-17185	139	22	model	model	NOUN
fcis-17185	139	23	related	relate	VERB
fcis-17185	139	24	parameters	parameter	NOUN
fcis-17185	139	25	,	,	PUNCT
fcis-17185	139	26	and	and	CCONJ
fcis-17185	139	27	set	set	VERB
fcis-17185	139	28	the	the	DET
fcis-17185	139	29	number	number	NOUN
fcis-17185	139	30	of	of	ADP
fcis-17185	139	31	neurons	neuron	NOUN
fcis-17185	139	32	in	in	ADP
fcis-17185	139	33	the	the	DET
fcis-17185	139	34	hidden	hide	VERB
fcis-17185	139	35	layer	layer	NOUN
fcis-17185	139	36	of	of	ADP
fcis-17185	139	37	the	the	DET
fcis-17185	139	38	elm	elm	PROPN
fcis-17185	139	39	.	.	PUNCT
fcis-17185	140	1	initialize	initialize	VERB
fcis-17185	140	2	p	p	PRON
fcis-17185	140	3	initial	initial	ADJ
fcis-17185	140	4	solutions	solution	NOUN
fcis-17185	140	5	,	,	PUNCT
fcis-17185	140	6	the	the	DET
fcis-17185	140	7	generated	generate	VERB
fcis-17185	140	8	initial	initial	ADJ
fcis-17185	140	9	solution	solution	NOUN
fcis-17185	140	10	dimensions	dimension	NOUN
fcis-17185	140	11	are	be	AUX
fcis-17185	140	12	*	*	PUNCT
fcis-17185	140	13	n	n	PRON
fcis-17185	140	14	l	l	NOUN
fcis-17185	140	15	l	l	PROPN
fcis-17185	140	16	.	.	PUNCT
fcis-17185	141	1	the	the	DET
fcis-17185	141	2	former	former	ADJ
fcis-17185	141	3	*	*	PUNCT
fcis-17185	141	4	n	n	NUM
fcis-17185	141	5	l	l	NOUN
fcis-17185	141	6	dimensions	dimension	NOUN
fcis-17185	141	7	denote	denote	VERB
fcis-17185	141	8	the	the	DET
fcis-17185	141	9	input	input	NOUN
fcis-17185	141	10	layer	layer	NOUN
fcis-17185	141	11	weight	weight	NOUN
fcis-17185	141	12	matrix	matrix	NOUN
fcis-17185	141	13	,	,	PUNCT
fcis-17185	141	14	and	and	CCONJ
fcis-17185	141	15	the	the	DET
fcis-17185	141	16	remaining	remain	VERB
fcis-17185	141	17	l	l	NOUN
fcis-17185	141	18	dimensions	dimension	NOUN
fcis-17185	141	19	denote	denote	VERB
fcis-17185	141	20	the	the	DET
fcis-17185	141	21	hidden	hide	VERB
fcis-17185	141	22	layer	layer	NOUN
fcis-17185	141	23	threshold	threshold	NOUN
fcis-17185	141	24	.	.	PUNCT
fcis-17185	142	1	step	step	NOUN
fcis-17185	142	2	2	2	NUM
fcis-17185	142	3	:	:	PUNCT
fcis-17185	142	4	input	input	VERB
fcis-17185	142	5	the	the	DET
fcis-17185	142	6	populationp	populationp	NOUN
fcis-17185	142	7	and	and	CCONJ
fcis-17185	142	8	the	the	DET
fcis-17185	142	9	training	training	NOUN
fcis-17185	142	10	set	set	VERB
fcis-17185	142	11	into	into	ADP
fcis-17185	142	12	the	the	DET
fcis-17185	142	13	67	67	NUM
fcis-17185	142	14	elm	elm	NOUN
fcis-17185	142	15	model	model	NOUN
fcis-17185	142	16	to	to	PART
fcis-17185	142	17	obtain	obtain	VERB
fcis-17185	142	18	the	the	DET
fcis-17185	142	19	weights	weights	ADJ
fcis-17185	142	20	and	and	CCONJ
fcis-17185	142	21	of	of	ADP
fcis-17185	142	22	the	the	DET
fcis-17185	142	23	output	output	NOUN
fcis-17185	142	24	layer	layer	NOUN
fcis-17185	142	25	and	and	CCONJ
fcis-17185	142	26	use	use	VERB
fcis-17185	142	27	the	the	DET
fcis-17185	142	28	network	network	NOUN
fcis-17185	142	29	test	test	NOUN
fcis-17185	142	30	accuracy	accuracy	NOUN
fcis-17185	142	31	as	as	ADP
fcis-17185	142	32	the	the	DET
fcis-17185	142	33	individual	individual	ADJ
fcis-17185	142	34	fitness	fitness	NOUN
fcis-17185	142	35	value	value	NOUN
fcis-17185	142	36	,	,	PUNCT
fcis-17185	142	37	and	and	CCONJ
fcis-17185	142	38	set	set	VERB
fcis-17185	142	39	the	the	DET
fcis-17185	142	40	optimal	optimal	ADJ
fcis-17185	142	41	individual	individual	NOUN
fcis-17185	142	42	as	as	ADP
fcis-17185	142	43	the	the	DET
fcis-17185	142	44	best	good	ADJ
fcis-17185	142	45	food	food	NOUN
fcis-17185	142	46	source	source	NOUN
fcis-17185	142	47	location	location	NOUN
fcis-17185	142	48	for	for	ADP
fcis-17185	142	49	the	the	DET
fcis-17185	142	50	current	current	ADJ
fcis-17185	142	51	population	population	NOUN
fcis-17185	142	52	.	.	PUNCT
fcis-17185	143	1	step	step	NOUN
fcis-17185	143	2	3	3	NUM
fcis-17185	143	3	:	:	PUNCT
fcis-17185	143	4	update	update	VERB
fcis-17185	143	5	the	the	DET
fcis-17185	143	6	leader	leader	NOUN
fcis-17185	143	7	location	location	NOUN
fcis-17185	143	8	information	information	NOUN
fcis-17185	143	9	according	accord	VERB
fcis-17185	143	10	to	to	ADP
fcis-17185	143	11	equation	equation	NOUN
fcis-17185	143	12	(	(	PUNCT
fcis-17185	143	13	6	6	NUM
fcis-17185	143	14	)	)	PUNCT
fcis-17185	143	15	and	and	CCONJ
fcis-17185	143	16	update	update	VERB
fcis-17185	143	17	the	the	DET
fcis-17185	143	18	follower	follower	NOUN
fcis-17185	143	19	location	location	NOUN
fcis-17185	143	20	information	information	NOUN
fcis-17185	143	21	according	accord	VERB
fcis-17185	143	22	to	to	ADP
fcis-17185	143	23	equation	equation	NOUN
fcis-17185	143	24	(	(	PUNCT
fcis-17185	143	25	12	12	NUM
fcis-17185	143	26	)	)	PUNCT
fcis-17185	143	27	and	and	CCONJ
fcis-17185	143	28	equation	equation	NOUN
fcis-17185	143	29	(	(	PUNCT
fcis-17185	143	30	13	13	NUM
fcis-17185	143	31	)	)	PUNCT
fcis-17185	143	32	and	and	CCONJ
fcis-17185	143	33	the	the	DET
fcis-17185	143	34	dual	dual	ADJ
fcis-17185	143	35	policy	policy	NOUN
fcis-17185	143	36	mechanism	mechanism	NOUN
fcis-17185	143	37	.	.	PUNCT
fcis-17185	144	1	step	step	NOUN
fcis-17185	144	2	4	4	NUM
fcis-17185	144	3	:	:	PUNCT
fcis-17185	144	4	calculate	calculate	VERB
fcis-17185	144	5	the	the	DET
fcis-17185	144	6	current	current	ADJ
fcis-17185	144	7	population	population	NOUN
fcis-17185	144	8	fitness	fitness	NOUN
fcis-17185	144	9	value	value	NOUN
fcis-17185	144	10	,	,	PUNCT
fcis-17185	144	11	check	check	VERB
fcis-17185	144	12	the	the	DET
fcis-17185	144	13	feasibility	feasibility	NOUN
fcis-17185	144	14	of	of	ADP
fcis-17185	144	15	the	the	DET
fcis-17185	144	16	new	new	ADJ
fcis-17185	144	17	position	position	NOUN
fcis-17185	144	18	,	,	PUNCT
fcis-17185	144	19	and	and	CCONJ
fcis-17185	144	20	obtain	obtain	VERB
fcis-17185	144	21	the	the	DET
fcis-17185	144	22	current	current	ADJ
fcis-17185	144	23	iteration	iteration	NOUN
fcis-17185	144	24	optimal	optimal	ADJ
fcis-17185	144	25	solution	solution	NOUN
fcis-17185	144	26	.	.	PUNCT
fcis-17185	145	1	step	step	NOUN
fcis-17185	145	2	5	5	NUM
fcis-17185	145	3	:	:	PUNCT
fcis-17185	145	4	determine	determine	VERB
fcis-17185	145	5	whether	whether	SCONJ
fcis-17185	145	6	the	the	DET
fcis-17185	145	7	algorithm	algorithm	NOUN
fcis-17185	145	8	reaches	reach	VERB
fcis-17185	145	9	the	the	DET
fcis-17185	145	10	maximum	maximum	ADJ
fcis-17185	145	11	number	number	NOUN
fcis-17185	145	12	of	of	ADP
fcis-17185	145	13	iterations	iteration	NOUN
fcis-17185	145	14	,	,	PUNCT
fcis-17185	145	15	if	if	SCONJ
fcis-17185	145	16	it	it	PRON
fcis-17185	145	17	meets	meet	VERB
fcis-17185	145	18	,	,	PUNCT
fcis-17185	145	19	use	use	VERB
fcis-17185	145	20	the	the	DET
fcis-17185	145	21	obtained	obtain	VERB
fcis-17185	145	22	individual	individual	ADJ
fcis-17185	145	23	optimal	optimal	ADJ
fcis-17185	145	24	solution	solution	NOUN
fcis-17185	145	25	as	as	ADP
fcis-17185	145	26	the	the	DET
fcis-17185	145	27	input	input	NOUN
fcis-17185	145	28	weights	weight	NOUN
fcis-17185	145	29	and	and	CCONJ
fcis-17185	145	30	thresholds	threshold	NOUN
fcis-17185	145	31	of	of	ADP
fcis-17185	145	32	the	the	DET
fcis-17185	145	33	elm	elm	NOUN
fcis-17185	145	34	model	model	NOUN
fcis-17185	145	35	to	to	PART
fcis-17185	145	36	train	train	VERB
fcis-17185	145	37	the	the	DET
fcis-17185	145	38	elm	elm	NOUN
fcis-17185	145	39	model	model	NOUN
fcis-17185	145	40	;	;	PUNCT
fcis-17185	145	41	otherwise	otherwise	ADV
fcis-17185	145	42	,	,	PUNCT
fcis-17185	145	43	return	return	VERB
fcis-17185	145	44	to	to	PART
fcis-17185	145	45	step	step	NOUN
fcis-17185	145	46	2	2	NUM
fcis-17185	145	47	.	.	NOUN
fcis-17185	145	48	5	5	NUM
fcis-17185	145	49	.	.	NOUN
fcis-17185	145	50	numerical	numerical	ADJ
fcis-17185	145	51	experiment	experiment	NOUN
fcis-17185	145	52	in	in	ADP
fcis-17185	145	53	this	this	DET
fcis-17185	145	54	section	section	NOUN
fcis-17185	145	55	,	,	PUNCT
fcis-17185	145	56	we	we	PRON
fcis-17185	145	57	use	use	VERB
fcis-17185	145	58	the	the	DET
fcis-17185	145	59	tssa	tssa	NOUN
fcis-17185	145	60	algorithm	algorithm	NOUN
fcis-17185	145	61	to	to	PART
fcis-17185	145	62	optimize	optimize	VERB
fcis-17185	145	63	the	the	DET
fcis-17185	145	64	input	input	NOUN
fcis-17185	145	65	layer	layer	NOUN
fcis-17185	145	66	weights	weight	NOUN
fcis-17185	145	67	of	of	ADP
fcis-17185	145	68	elm	elm	NOUN
fcis-17185	145	69	for	for	ADP
fcis-17185	145	70	classification	classification	NOUN
fcis-17185	145	71	purposes	purpose	NOUN
fcis-17185	145	72	.	.	PUNCT
fcis-17185	146	1	we	we	PRON
fcis-17185	146	2	perform	perform	VERB
fcis-17185	146	3	classification	classification	NOUN
fcis-17185	146	4	experiments	experiment	NOUN
fcis-17185	146	5	of	of	ADP
fcis-17185	146	6	the	the	DET
fcis-17185	146	7	proposed	propose	VERB
fcis-17185	146	8	new	new	ADJ
fcis-17185	146	9	algorithm	algorithm	NOUN
fcis-17185	146	10	on	on	ADP
fcis-17185	146	11	four	four	NUM
fcis-17185	146	12	real	real	ADJ
fcis-17185	146	13	datasets	dataset	NOUN
fcis-17185	146	14	and	and	CCONJ
fcis-17185	146	15	compare	compare	VERB
fcis-17185	146	16	it	it	PRON
fcis-17185	146	17	with	with	ADP
fcis-17185	146	18	elm	elm	PROPN
fcis-17185	146	19	,	,	PUNCT
fcis-17185	146	20	ssa	ssa	NOUN
fcis-17185	146	21	-	-	PUNCT
fcis-17185	146	22	elm	elm	PROPN
fcis-17185	146	23	,	,	PUNCT
fcis-17185	146	24	algorithms	algorithm	NOUN
fcis-17185	146	25	.	.	PUNCT
fcis-17185	147	1	using	use	VERB
fcis-17185	147	2	the	the	DET
fcis-17185	147	3	cross	cross	ADJ
fcis-17185	147	4	-	-	ADJ
fcis-17185	147	5	validation	validation	ADJ
fcis-17185	147	6	method	method	NOUN
fcis-17185	147	7	,	,	PUNCT
fcis-17185	147	8	70	70	NUM
fcis-17185	147	9	%	%	NOUN
fcis-17185	147	10	of	of	ADP
fcis-17185	147	11	each	each	DET
fcis-17185	147	12	dataset	dataset	NOUN
fcis-17185	147	13	is	be	AUX
fcis-17185	147	14	selected	select	VERB
fcis-17185	147	15	as	as	ADP
fcis-17185	147	16	the	the	DET
fcis-17185	147	17	training	training	NOUN
fcis-17185	147	18	set	set	NOUN
fcis-17185	147	19	and	and	CCONJ
fcis-17185	147	20	the	the	DET
fcis-17185	147	21	rest	rest	NOUN
fcis-17185	147	22	as	as	SCONJ
fcis-17185	147	23	the	the	DET
fcis-17185	147	24	test	test	NOUN
fcis-17185	147	25	set	set	VERB
fcis-17185	147	26	each	each	DET
fcis-17185	147	27	time	time	NOUN
fcis-17185	147	28	to	to	PART
fcis-17185	147	29	obtain	obtain	VERB
fcis-17185	147	30	the	the	DET
fcis-17185	147	31	average	average	ADJ
fcis-17185	147	32	results	result	NOUN
fcis-17185	147	33	of	of	ADP
fcis-17185	147	34	each	each	DET
fcis-17185	147	35	algorithm	algorithm	NOUN
fcis-17185	147	36	under	under	ADP
fcis-17185	147	37	10	10	NUM
fcis-17185	147	38	simulation	simulation	NOUN
fcis-17185	147	39	experiments	experiment	NOUN
fcis-17185	147	40	,	,	PUNCT
fcis-17185	147	41	and	and	CCONJ
fcis-17185	147	42	then	then	ADV
fcis-17185	147	43	their	their	PRON
fcis-17185	147	44	performance	performance	NOUN
fcis-17185	147	45	is	be	AUX
fcis-17185	147	46	analyzed	analyze	VERB
fcis-17185	147	47	and	and	CCONJ
fcis-17185	147	48	compared	compare	VERB
fcis-17185	147	49	in	in	ADP
fcis-17185	147	50	detail	detail	NOUN
fcis-17185	147	51	.	.	PUNCT
fcis-17185	148	1	the	the	DET
fcis-17185	148	2	same	same	ADJ
fcis-17185	148	3	network	network	NOUN
fcis-17185	148	4	structure	structure	NOUN
fcis-17185	148	5	is	be	AUX
fcis-17185	148	6	used	use	VERB
fcis-17185	148	7	for	for	ADP
fcis-17185	148	8	testing	testing	NOUN
fcis-17185	148	9	in	in	ADP
fcis-17185	148	10	each	each	DET
fcis-17185	148	11	experiment	experiment	NOUN
fcis-17185	148	12	,	,	PUNCT
fcis-17185	148	13	the	the	DET
fcis-17185	148	14	number	number	NOUN
fcis-17185	148	15	of	of	ADP
fcis-17185	148	16	hidden	hide	VERB
fcis-17185	148	17	layer	layer	NOUN
fcis-17185	148	18	neurons	neuron	NOUN
fcis-17185	148	19	is	be	AUX
fcis-17185	148	20	fixed	fix	VERB
fcis-17185	148	21	to	to	ADP
fcis-17185	148	22	3	3	NUM
fcis-17185	148	23	,	,	PUNCT
fcis-17185	148	24	the	the	DET
fcis-17185	148	25	population	population	NOUN
fcis-17185	148	26	sizep	sizep	VERB
fcis-17185	148	27	is	be	AUX
fcis-17185	148	28	set	set	VERB
fcis-17185	148	29	to	to	ADP
fcis-17185	148	30	50	50	NUM
fcis-17185	148	31	,	,	PUNCT
fcis-17185	148	32	and	and	CCONJ
fcis-17185	148	33	the	the	DET
fcis-17185	148	34	maximum	maximum	ADJ
fcis-17185	148	35	number	number	NOUN
fcis-17185	148	36	of	of	ADP
fcis-17185	148	37	iterations	iteration	NOUN
fcis-17185	148	38	is	be	AUX
fcis-17185	148	39	100.the	100.the	NUM
fcis-17185	148	40	datasets	dataset	NOUN
fcis-17185	148	41	for	for	ADP
fcis-17185	148	42	the	the	DET
fcis-17185	148	43	classification	classification	NOUN
fcis-17185	148	44	experiments	experiment	NOUN
fcis-17185	148	45	are	be	AUX
fcis-17185	148	46	all	all	ADV
fcis-17185	148	47	from	from	ADP
fcis-17185	148	48	the	the	DET
fcis-17185	148	49	uci	uci	PROPN
fcis-17185	148	50	machine	machine	NOUN
fcis-17185	148	51	learning	learn	VERB
fcis-17185	148	52	repository	repository	NOUN
fcis-17185	148	53	,	,	PUNCT
fcis-17185	148	54	and	and	CCONJ
fcis-17185	148	55	the	the	DET
fcis-17185	148	56	corresponding	corresponding	ADJ
fcis-17185	148	57	experimental	experimental	ADJ
fcis-17185	148	58	results	result	NOUN
fcis-17185	148	59	are	be	AUX
fcis-17185	148	60	shown	show	VERB
fcis-17185	148	61	in	in	ADP
fcis-17185	148	62	table	table	NOUN
fcis-17185	149	1	1	1	NUM
fcis-17185	149	2	.	.	PUNCT
fcis-17185	149	3	table	table	NOUN
fcis-17185	149	4	1	1	NUM
fcis-17185	149	5	.	.	PUNCT
fcis-17185	150	1	classification	classification	NOUN
fcis-17185	150	2	test	test	NOUN
fcis-17185	150	3	results	result	NOUN
fcis-17185	150	4	data	datum	NOUN
fcis-17185	150	5	sat	sit	VERB
fcis-17185	150	6	accuracy	accuracy	NOUN
fcis-17185	150	7	rate	rate	NOUN
fcis-17185	150	8	tssaelm	tssaelm	PROPN
fcis-17185	150	9	ssaelm	ssaelm	PROPN
fcis-17185	150	10	elm	elm	PROPN
fcis-17185	150	11	seeds	seed	NOUN
fcis-17185	150	12	training	train	VERB
fcis-17185	150	13	0.9419	0.9419	NUM
fcis-17185	150	14	0.9516	0.9516	NUM
fcis-17185	150	15	0.9410	0.9410	NUM
fcis-17185	150	16	testing	test	VERB
fcis-17185	150	17	0.9330	0.9330	NUM
fcis-17185	150	18	0.9232	0.9232	NUM
fcis-17185	150	19	0.9200	0.9200	NUM
fcis-17185	150	20	tae	tae	NOUN
fcis-17185	150	21	training	train	VERB
fcis-17185	150	22	1.0000	1.0000	NUM
fcis-17185	150	23	0.9823	0.9823	NUM
fcis-17185	150	24	0.9647	0.9647	NUM
fcis-17185	150	25	testing	testing	NOUN
fcis-17185	150	26	0.9562	0.9562	NUM
fcis-17185	150	27	0.9485	0.9485	NUM
fcis-17185	150	28	0.9300	0.9300	NUM
fcis-17185	150	29	iris	iris	NOUN
fcis-17185	150	30	training	train	VERB
fcis-17185	150	31	1.0000	1.0000	NUM
fcis-17185	150	32	1.0000	1.0000	NUM
fcis-17185	150	33	1.0000	1.0000	NUM
fcis-17185	150	34	testing	testing	NOUN
fcis-17185	150	35	0.9824	0.9824	NUM
fcis-17185	150	36	0.9710	0.9710	NUM
fcis-17185	150	37	0.9520	0.9520	NUM
fcis-17185	150	38	cleveland	cleveland	NOUN
fcis-17185	150	39	training	train	VERB
fcis-17185	150	40	0.9272	0.9272	NUM
fcis-17185	150	41	0.9141	0.9141	NUM
fcis-17185	150	42	0.8992	0.8992	NUM
fcis-17185	150	43	testing	test	VERB
fcis-17185	150	44	0.9088	0.9088	NUM
fcis-17185	150	45	0.8605	0.8605	NUM
fcis-17185	150	46	0.8048	0.8048	NUM
fcis-17185	150	47	as	as	SCONJ
fcis-17185	150	48	can	can	AUX
fcis-17185	150	49	be	be	AUX
fcis-17185	150	50	seen	see	VERB
fcis-17185	150	51	from	from	ADP
fcis-17185	150	52	the	the	DET
fcis-17185	150	53	experimental	experimental	ADJ
fcis-17185	150	54	results	result	NOUN
fcis-17185	150	55	in	in	ADP
fcis-17185	150	56	table	table	NOUN
fcis-17185	150	57	1	1	NUM
fcis-17185	150	58	,	,	PUNCT
fcis-17185	150	59	the	the	DET
fcis-17185	150	60	tssa	tssa	NOUN
fcis-17185	150	61	algorithm	algorithm	NOUN
fcis-17185	150	62	proposed	propose	VERB
fcis-17185	150	63	in	in	ADP
fcis-17185	150	64	this	this	DET
fcis-17185	150	65	paper	paper	NOUN
fcis-17185	150	66	consistently	consistently	ADV
fcis-17185	150	67	gives	give	VERB
fcis-17185	150	68	the	the	DET
fcis-17185	150	69	best	good	ADJ
fcis-17185	150	70	test	test	NOUN
fcis-17185	150	71	results	result	NOUN
fcis-17185	150	72	for	for	ADP
fcis-17185	150	73	the	the	DET
fcis-17185	150	74	four	four	NUM
fcis-17185	150	75	high	high	ADJ
fcis-17185	150	76	or	or	CCONJ
fcis-17185	150	77	low	low	ADJ
fcis-17185	150	78	feature	feature	NOUN
fcis-17185	150	79	classification	classification	NOUN
fcis-17185	150	80	datasets	dataset	NOUN
fcis-17185	150	81	.	.	PUNCT
fcis-17185	151	1	it	it	PRON
fcis-17185	151	2	is	be	AUX
fcis-17185	151	3	worth	worth	ADJ
fcis-17185	151	4	noting	note	VERB
fcis-17185	151	5	that	that	SCONJ
fcis-17185	151	6	tssa	tssa	NOUN
fcis-17185	151	7	-	-	PUNCT
fcis-17185	151	8	elm	elm	NOUN
fcis-17185	151	9	improves	improve	VERB
fcis-17185	151	10	the	the	DET
fcis-17185	151	11	training	training	NOUN
fcis-17185	151	12	test	test	NOUN
fcis-17185	151	13	accuracy	accuracy	NOUN
fcis-17185	151	14	by	by	ADP
fcis-17185	151	15	2.9725	2.9725	NUM
fcis-17185	151	16	%	%	NOUN
fcis-17185	151	17	compared	compare	VERB
fcis-17185	151	18	with	with	ADP
fcis-17185	151	19	elm	elm	NOUN
fcis-17185	151	20	and	and	CCONJ
fcis-17185	151	21	1.2288	1.2288	NUM
fcis-17185	151	22	%	%	NOUN
fcis-17185	151	23	compared	compare	VERB
fcis-17185	151	24	with	with	ADP
fcis-17185	151	25	ssa	ssa	NOUN
fcis-17185	151	26	-	-	PUNCT
fcis-17185	151	27	elm	elm	NOUN
fcis-17185	151	28	.	.	PUNCT
fcis-17185	152	1	the	the	DET
fcis-17185	152	2	above	above	ADJ
fcis-17185	152	3	results	result	NOUN
fcis-17185	152	4	can	can	AUX
fcis-17185	152	5	indicate	indicate	VERB
fcis-17185	152	6	that	that	DET
fcis-17185	152	7	tssa	tssa	NOUN
fcis-17185	152	8	has	have	VERB
fcis-17185	152	9	better	well	ADJ
fcis-17185	152	10	generalization	generalization	NOUN
fcis-17185	152	11	performance	performance	NOUN
fcis-17185	152	12	and	and	CCONJ
fcis-17185	152	13	stability	stability	NOUN
fcis-17185	152	14	,	,	PUNCT
fcis-17185	152	15	and	and	CCONJ
fcis-17185	152	16	can	can	AUX
fcis-17185	152	17	effectively	effectively	ADV
fcis-17185	152	18	train	train	VERB
fcis-17185	152	19	elm	elm	NOUN
fcis-17185	152	20	to	to	PART
fcis-17185	152	21	deal	deal	VERB
fcis-17185	152	22	with	with	ADP
fcis-17185	152	23	classification	classification	NOUN
fcis-17185	152	24	problems	problem	NOUN
fcis-17185	152	25	.	.	PUNCT
fcis-17185	153	1	(	(	PUNCT
fcis-17185	153	2	a	a	X
fcis-17185	153	3	)	)	PUNCT
fcis-17185	153	4	seeds	seed	NOUN
fcis-17185	153	5	(	(	PUNCT
fcis-17185	153	6	b	b	NOUN
fcis-17185	153	7	)	)	PUNCT
fcis-17185	153	8	tae	tae	NOUN
fcis-17185	153	9	(	(	PUNCT
fcis-17185	153	10	c	c	NOUN
fcis-17185	153	11	)	)	PUNCT
fcis-17185	153	12	iris	iris	NOUN
fcis-17185	153	13	(	(	PUNCT
fcis-17185	153	14	d	d	NOUN
fcis-17185	153	15	)	)	PUNCT
fcis-17185	153	16	cleveland	cleveland	PROPN
fcis-17185	153	17	figure	figure	NOUN
fcis-17185	153	18	3	3	NUM
fcis-17185	153	19	.	.	PUNCT
fcis-17185	153	20	classification	classification	NOUN
fcis-17185	153	21	accuracy	accuracy	NOUN
fcis-17185	153	22	of	of	ADP
fcis-17185	153	23	different	different	ADJ
fcis-17185	153	24	datasets	dataset	NOUN
fcis-17185	153	25	with	with	ADP
fcis-17185	153	26	different	different	ADJ
fcis-17185	153	27	hidden	hide	VERB
fcis-17185	153	28	layer	layer	NOUN
fcis-17185	153	29	neuron	neuron	NOUN
fcis-17185	153	30	nodes	nod	VERB
fcis-17185	153	31	68	68	NUM
fcis-17185	153	32	figure	figure	NOUN
fcis-17185	153	33	3	3	NUM
fcis-17185	153	34	gives	give	VERB
fcis-17185	153	35	the	the	DET
fcis-17185	153	36	classification	classification	NOUN
fcis-17185	153	37	accuracy	accuracy	NOUN
fcis-17185	153	38	of	of	ADP
fcis-17185	153	39	different	different	ADJ
fcis-17185	153	40	datasets	dataset	NOUN
fcis-17185	153	41	under	under	ADP
fcis-17185	153	42	different	different	ADJ
fcis-17185	153	43	numbers	number	NOUN
fcis-17185	153	44	of	of	ADP
fcis-17185	153	45	hidden	hidden	ADJ
fcis-17185	153	46	layer	layer	NOUN
fcis-17185	153	47	neurons	neuron	NOUN
fcis-17185	153	48	,	,	PUNCT
fcis-17185	153	49	and	and	CCONJ
fcis-17185	153	50	the	the	DET
fcis-17185	153	51	experimental	experimental	ADJ
fcis-17185	153	52	results	result	NOUN
fcis-17185	153	53	show	show	VERB
fcis-17185	153	54	that	that	SCONJ
fcis-17185	153	55	with	with	ADP
fcis-17185	153	56	the	the	DET
fcis-17185	153	57	increase	increase	NOUN
fcis-17185	153	58	of	of	ADP
fcis-17185	153	59	the	the	DET
fcis-17185	153	60	number	number	NOUN
fcis-17185	153	61	of	of	ADP
fcis-17185	153	62	hidden	hide	VERB
fcis-17185	153	63	layer	layer	NOUN
fcis-17185	153	64	neuron	neuron	NOUN
fcis-17185	153	65	nodes	nod	VERB
fcis-17185	153	66	the	the	DET
fcis-17185	153	67	classification	classification	NOUN
fcis-17185	153	68	accuracy	accuracy	NOUN
fcis-17185	153	69	is	be	AUX
fcis-17185	153	70	also	also	ADV
fcis-17185	153	71	on	on	ADP
fcis-17185	153	72	the	the	DET
fcis-17185	153	73	rise	rise	NOUN
fcis-17185	153	74	,	,	PUNCT
fcis-17185	153	75	but	but	CCONJ
fcis-17185	153	76	the	the	DET
fcis-17185	153	77	rise	rise	NOUN
fcis-17185	153	78	reaches	reach	VERB
fcis-17185	153	79	a	a	DET
fcis-17185	153	80	certain	certain	ADJ
fcis-17185	153	81	accuracy	accuracy	NOUN
fcis-17185	153	82	and	and	CCONJ
fcis-17185	153	83	then	then	ADV
fcis-17185	153	84	will	will	AUX
fcis-17185	153	85	remain	remain	VERB
fcis-17185	153	86	stable	stable	ADJ
fcis-17185	153	87	.	.	PUNCT
fcis-17185	154	1	compared	compare	VERB
fcis-17185	154	2	with	with	ADP
fcis-17185	154	3	its	its	PRON
fcis-17185	154	4	comparison	comparison	NOUN
fcis-17185	154	5	algorithm	algorithm	NOUN
fcis-17185	154	6	,	,	PUNCT
fcis-17185	154	7	the	the	DET
fcis-17185	154	8	change	change	NOUN
fcis-17185	154	9	of	of	ADP
fcis-17185	154	10	the	the	DET
fcis-17185	154	11	hidden	hide	VERB
fcis-17185	154	12	layer	layer	NOUN
fcis-17185	154	13	nodes	node	NOUN
fcis-17185	154	14	has	have	VERB
fcis-17185	154	15	less	less	ADJ
fcis-17185	154	16	influence	influence	NOUN
fcis-17185	154	17	on	on	ADP
fcis-17185	154	18	ssa	ssa	NOUN
fcis-17185	154	19	-	-	PUNCT
fcis-17185	154	20	elm	elm	NOUN
fcis-17185	154	21	,	,	PUNCT
fcis-17185	154	22	and	and	CCONJ
fcis-17185	154	23	its	its	PRON
fcis-17185	154	24	experimental	experimental	ADJ
fcis-17185	154	25	results	result	NOUN
fcis-17185	154	26	remain	remain	VERB
fcis-17185	154	27	stable	stable	ADJ
fcis-17185	154	28	,	,	PUNCT
fcis-17185	154	29	which	which	PRON
fcis-17185	154	30	indicates	indicate	VERB
fcis-17185	154	31	that	that	SCONJ
fcis-17185	154	32	ssa	ssa	NOUN
fcis-17185	154	33	-	-	PUNCT
fcis-17185	154	34	elm	elm	NOUN
fcis-17185	154	35	has	have	VERB
fcis-17185	154	36	better	well	ADJ
fcis-17185	154	37	stability	stability	NOUN
fcis-17185	154	38	.	.	PUNCT
fcis-17185	155	1	6	6	X
fcis-17185	155	2	.	.	X
fcis-17185	155	3	conclusion	conclusion	NOUN
fcis-17185	155	4	in	in	ADP
fcis-17185	155	5	order	order	NOUN
fcis-17185	155	6	to	to	PART
fcis-17185	155	7	mitigate	mitigate	VERB
fcis-17185	155	8	the	the	DET
fcis-17185	155	9	impact	impact	NOUN
fcis-17185	155	10	of	of	ADP
fcis-17185	155	11	the	the	DET
fcis-17185	155	12	randomness	randomness	NOUN
fcis-17185	155	13	in	in	ADP
fcis-17185	155	14	the	the	DET
fcis-17185	155	15	input	input	NOUN
fcis-17185	155	16	weights	weight	NOUN
fcis-17185	155	17	and	and	CCONJ
fcis-17185	155	18	thresholds	threshold	NOUN
fcis-17185	155	19	of	of	ADP
fcis-17185	155	20	the	the	DET
fcis-17185	155	21	extreme	extreme	ADJ
fcis-17185	155	22	learning	learning	NOUN
fcis-17185	155	23	machine	machine	NOUN
fcis-17185	155	24	(	(	PUNCT
fcis-17185	155	25	elm	elm	NOUN
fcis-17185	155	26	)	)	PUNCT
fcis-17185	155	27	model	model	NOUN
fcis-17185	155	28	,	,	PUNCT
fcis-17185	155	29	this	this	DET
fcis-17185	155	30	paper	paper	NOUN
fcis-17185	155	31	proposes	propose	VERB
fcis-17185	155	32	a	a	DET
fcis-17185	155	33	twin	twin	ADJ
fcis-17185	155	34	strategy	strategy	NOUN
fcis-17185	155	35	salp	salp	NOUN
fcis-17185	155	36	swarm	swarm	NOUN
fcis-17185	155	37	algorithm	algorithm	NOUN
fcis-17185	155	38	(	(	PUNCT
fcis-17185	155	39	tssa	tssa	NOUN
fcis-17185	155	40	)	)	PUNCT
fcis-17185	155	41	to	to	PART
fcis-17185	155	42	optimize	optimize	VERB
fcis-17185	155	43	the	the	DET
fcis-17185	155	44	input	input	NOUN
fcis-17185	155	45	weights	weight	NOUN
fcis-17185	155	46	and	and	CCONJ
fcis-17185	155	47	thresholds	threshold	NOUN
fcis-17185	155	48	of	of	ADP
fcis-17185	155	49	elm	elm	PROPN
fcis-17185	155	50	.	.	PUNCT
fcis-17185	156	1	additionally	additionally	ADV
fcis-17185	156	2	,	,	PUNCT
fcis-17185	156	3	a	a	DET
fcis-17185	156	4	chaotic	chaotic	ADJ
fcis-17185	156	5	initialization	initialization	NOUN
fcis-17185	156	6	method	method	NOUN
fcis-17185	156	7	is	be	AUX
fcis-17185	156	8	introduced	introduce	VERB
fcis-17185	156	9	to	to	PART
fcis-17185	156	10	generate	generate	VERB
fcis-17185	156	11	more	more	ADV
fcis-17185	156	12	diverse	diverse	ADJ
fcis-17185	156	13	initial	initial	ADJ
fcis-17185	156	14	solutions	solution	NOUN
fcis-17185	156	15	in	in	ADP
fcis-17185	156	16	the	the	DET
fcis-17185	156	17	early	early	ADJ
fcis-17185	156	18	stages	stage	NOUN
fcis-17185	156	19	of	of	ADP
fcis-17185	156	20	the	the	DET
fcis-17185	156	21	algorithm	algorithm	NOUN
fcis-17185	156	22	.	.	PUNCT
fcis-17185	157	1	through	through	ADP
fcis-17185	157	2	experimental	experimental	ADJ
fcis-17185	157	3	validation	validation	NOUN
fcis-17185	157	4	,	,	PUNCT
fcis-17185	157	5	the	the	DET
fcis-17185	157	6	proposed	propose	VERB
fcis-17185	157	7	algorithm	algorithm	NOUN
fcis-17185	157	8	effectively	effectively	ADV
fcis-17185	157	9	balances	balance	VERB
fcis-17185	157	10	the	the	DET
fcis-17185	157	11	exploration	exploration	NOUN
fcis-17185	157	12	and	and	CCONJ
fcis-17185	157	13	exploitation	exploitation	NOUN
fcis-17185	157	14	capabilities	capability	NOUN
fcis-17185	157	15	,	,	PUNCT
fcis-17185	157	16	achieving	achieve	VERB
fcis-17185	157	17	optimization	optimization	NOUN
fcis-17185	157	18	of	of	ADP
fcis-17185	157	19	the	the	DET
fcis-17185	157	20	weights	weight	NOUN
fcis-17185	157	21	and	and	CCONJ
fcis-17185	157	22	thresholds	threshold	NOUN
fcis-17185	157	23	in	in	ADP
fcis-17185	157	24	elm	elm	PROPN
fcis-17185	157	25	.	.	PUNCT
fcis-17185	158	1	furthermore	furthermore	ADV
fcis-17185	158	2	,	,	PUNCT
fcis-17185	158	3	classification	classification	NOUN
fcis-17185	158	4	tests	test	NOUN
fcis-17185	158	5	on	on	ADP
fcis-17185	158	6	uci	uci	PROPN
fcis-17185	158	7	machine	machine	NOUN
fcis-17185	158	8	learning	learning	AUX
fcis-17185	158	9	repository	repository	VERB
fcis-17185	158	10	its	its	PRON
fcis-17185	158	11	good	good	ADJ
fcis-17185	158	12	generalization	generalization	NOUN
fcis-17185	158	13	performance	performance	NOUN
fcis-17185	158	14	and	and	CCONJ
fcis-17185	158	15	stability	stability	NOUN
fcis-17185	158	16	.	.	PUNCT
fcis-17185	159	1	references	reference	NOUN
fcis-17185	159	2	[	[	X
fcis-17185	159	3	1	1	X
fcis-17185	159	4	]	]	X
fcis-17185	159	5	huang	huang	PROPN
fcis-17185	159	6	g	g	PROPN
fcis-17185	159	7	,	,	PUNCT
fcis-17185	159	8	zhu	zhu	PROPN
fcis-17185	159	9	q	q	PROPN
fcis-17185	159	10	,	,	PUNCT
fcis-17185	159	11	siew	siew	PROPN
fcis-17185	159	12	c.	c.	PROPN
fcis-17185	159	13	extreme	extreme	PROPN
fcis-17185	159	14	learning	learn	VERB
fcis-17185	159	15	machine	machine	NOUN
fcis-17185	159	16	:	:	PUNCT
fcis-17185	159	17	a	a	DET
fcis-17185	159	18	new	new	ADJ
fcis-17185	159	19	learning	learning	NOUN
fcis-17185	159	20	scheme	scheme	NOUN
fcis-17185	159	21	of	of	ADP
fcis-17185	159	22	feedforward	feedforward	NOUN
fcis-17185	159	23	neural	neural	ADJ
fcis-17185	159	24	networks[j	networks[j	PROPN
fcis-17185	159	25	]	]	PUNCT
fcis-17185	159	26	.	.	PUNCT
fcis-17185	160	1	proceedings	proceeding	NOUN
fcis-17185	160	2	international	international	ADJ
fcis-17185	160	3	joint	joint	ADJ
fcis-17185	160	4	conference	conference	NOUN
fcis-17185	160	5	neural	neural	ADJ
fcis-17185	160	6	networks	network	NOUN
fcis-17185	160	7	,	,	PUNCT
fcis-17185	160	8	2004	2004	NUM
fcis-17185	160	9	,	,	PUNCT
fcis-17185	160	10	2	2	NUM
fcis-17185	160	11	:	:	PUNCT
fcis-17185	160	12	985	985	NUM
fcis-17185	160	13	-	-	SYM
fcis-17185	160	14	990	990	NUM
fcis-17185	160	15	.	.	PUNCT
fcis-17185	161	1	[	[	X
fcis-17185	161	2	2	2	NUM
fcis-17185	161	3	]	]	X
fcis-17185	161	4	zhao	zhao	PROPN
fcis-17185	161	5	y	y	PROPN
fcis-17185	161	6	,	,	PUNCT
fcis-17185	161	7	huerta	huerta	PROPN
fcis-17185	161	8	r.	r.	PROPN
fcis-17185	161	9	improvements	improvement	NOUN
fcis-17185	161	10	on	on	ADP
fcis-17185	161	11	parsimonious	parsimonious	ADJ
fcis-17185	161	12	extreme	extreme	ADJ
fcis-17185	161	13	learning	learning	NOUN
fcis-17185	161	14	machine	machine	NOUN
fcis-17185	161	15	using	use	VERB
fcis-17185	161	16	recursive	recursive	ADJ
fcis-17185	161	17	orthogonal	orthogonal	ADJ
fcis-17185	161	18	least	least	ADJ
fcis-17185	161	19	squares[j	squares[j	NOUN
fcis-17185	161	20	]	]	X
fcis-17185	161	21	.	.	PUNCT
fcis-17185	162	1	neurocomputing	neurocomputing	NOUN
fcis-17185	162	2	,	,	PUNCT
fcis-17185	162	3	2016	2016	NUM
fcis-17185	162	4	,	,	PUNCT
fcis-17185	162	5	191	191	NUM
fcis-17185	162	6	:	:	SYM
fcis-17185	162	7	82	82	NUM
fcis-17185	162	8	-	-	SYM
fcis-17185	162	9	94	94	NUM
fcis-17185	162	10	.	.	PUNCT
fcis-17185	163	1	[	[	X
fcis-17185	163	2	3	3	X
fcis-17185	163	3	]	]	X
fcis-17185	163	4	han	han	PROPN
fcis-17185	163	5	m	m	PROPN
fcis-17185	163	6	,	,	PUNCT
fcis-17185	163	7	yang	yang	PROPN
fcis-17185	163	8	x	x	PROPN
fcis-17185	163	9	,	,	PUNCT
fcis-17185	163	10	jiang	jiang	PROPN
fcis-17185	163	11	e.	e.	PROPN
fcis-17185	163	12	an	an	DET
fcis-17185	163	13	extreme	extreme	ADJ
fcis-17185	163	14	learning	learning	NOUN
fcis-17185	163	15	machine	machine	NOUN
fcis-17185	163	16	based	base	VERB
fcis-17185	163	17	on	on	ADP
fcis-17185	163	18	celluar	celluar	ADJ
fcis-17185	163	19	automata	automata	NOUN
fcis-17185	163	20	of	of	ADP
fcis-17185	163	21	edge	edge	NOUN
fcis-17185	163	22	detection	detection	NOUN
fcis-17185	163	23	for	for	ADP
fcis-17185	163	24	remote	remote	ADJ
fcis-17185	163	25	sensing	sensing	NOUN
fcis-17185	163	26	images	image	NOUN
fcis-17185	164	1	[	[	X
fcis-17185	164	2	j	j	X
fcis-17185	164	3	]	]	X
fcis-17185	164	4	.	.	PUNCT
fcis-17185	165	1	neurocomputing	neurocomputing	NOUN
fcis-17185	165	2	,	,	PUNCT
fcis-17185	165	3	2016	2016	NUM
fcis-17185	165	4	,	,	PUNCT
fcis-17185	165	5	198	198	NUM
fcis-17185	165	6	:	:	PUNCT
fcis-17185	165	7	27	27	NUM
fcis-17185	165	8	-	-	SYM
fcis-17185	165	9	34	34	NUM
fcis-17185	165	10	.	.	PUNCT
fcis-17185	166	1	[	[	X
fcis-17185	166	2	4	4	NUM
fcis-17185	166	3	]	]	X
fcis-17185	166	4	jeddi	jeddi	PROPN
fcis-17185	166	5	s	s	PROPN
fcis-17185	166	6	,	,	PUNCT
fcis-17185	166	7	sharifian	sharifian	PROPN
fcis-17185	166	8	s.	s.	PROPN
fcis-17185	166	9	a	a	DET
fcis-17185	166	10	hybrid	hybrid	ADJ
fcis-17185	166	11	wavelet	wavelet	NOUN
fcis-17185	166	12	decomposer	decomposer	NOUN
fcis-17185	166	13	and	and	CCONJ
fcis-17185	166	14	gmdh	gmdh	ADJ
fcis-17185	166	15	-	-	PUNCT
fcis-17185	166	16	elm	elm	NOUN
fcis-17185	166	17	ensemble	ensemble	ADJ
fcis-17185	166	18	model	model	NOUN
fcis-17185	166	19	for	for	ADP
fcis-17185	166	20	network	network	NOUN
fcis-17185	166	21	function	function	NOUN
fcis-17185	166	22	virtualization	virtualization	NOUN
fcis-17185	166	23	workload	workload	NOUN
fcis-17185	166	24	forecasting	forecasting	NOUN
fcis-17185	166	25	in	in	ADP
fcis-17185	166	26	cloud	cloud	ADJ
fcis-17185	166	27	computing[j	computing[j	NOUN
fcis-17185	166	28	]	]	PUNCT
fcis-17185	166	29	.	.	PUNCT
fcis-17185	167	1	applied	apply	VERB
fcis-17185	167	2	soft	soft	ADJ
fcis-17185	167	3	computing	computing	NOUN
fcis-17185	167	4	,	,	PUNCT
fcis-17185	167	5	2019	2019	NUM
fcis-17185	167	6	,	,	PUNCT
fcis-17185	167	7	105940	105940	NUM
fcis-17185	167	8	.	.	PUNCT
fcis-17185	168	1	[	[	X
fcis-17185	168	2	5	5	NUM
fcis-17185	168	3	]	]	SYM
fcis-17185	168	4	altay	altay	NOUN
fcis-17185	168	5	o	o	PROPN
fcis-17185	168	6	,	,	PUNCT
fcis-17185	168	7	ulas	ulas	PROPN
fcis-17185	168	8	m	m	PROPN
fcis-17185	168	9	,	,	PUNCT
fcis-17185	168	10	alyamac	alyamac	PROPN
fcis-17185	168	11	k.	k.	PROPN
fcis-17185	168	12	dcs	dcs	PROPN
fcis-17185	168	13	-	-	PROPN
fcis-17185	168	14	elm	elm	PROPN
fcis-17185	168	15	:	:	PUNCT
fcis-17185	168	16	a	a	DET
fcis-17185	168	17	novel	novel	ADJ
fcis-17185	168	18	method	method	NOUN
fcis-17185	168	19	for	for	ADP
fcis-17185	168	20	extreme	extreme	ADJ
fcis-17185	168	21	learning	learning	NOUN
fcis-17185	168	22	machine	machine	NOUN
fcis-17185	168	23	for	for	ADP
fcis-17185	168	24	regression	regression	NOUN
fcis-17185	168	25	problems	problem	NOUN
fcis-17185	168	26	and	and	CCONJ
fcis-17185	168	27	a	a	DET
fcis-17185	168	28	new	new	ADJ
fcis-17185	168	29	approach	approach	NOUN
fcis-17185	168	30	for	for	ADP
fcis-17185	168	31	the	the	DET
fcis-17185	168	32	sfrscc[j	sfrscc[j	NOUN
fcis-17185	168	33	]	]	PUNCT
fcis-17185	168	34	.	.	PUNCT
fcis-17185	169	1	peerj	peerj	PROPN
fcis-17185	169	2	comput	comput	PROPN
fcis-17185	169	3	science	science	NOUN
fcis-17185	169	4	,	,	PUNCT
fcis-17185	169	5	2021	2021	NUM
fcis-17185	169	6	,	,	PUNCT
fcis-17185	169	7	7	7	NUM
fcis-17185	169	8	.	.	PUNCT
fcis-17185	170	1	[	[	X
fcis-17185	170	2	6	6	NUM
fcis-17185	170	3	]	]	X
fcis-17185	170	4	fan	fan	PROPN
fcis-17185	170	5	j	j	PROPN
fcis-17185	170	6	,	,	PUNCT
fcis-17185	170	7	sun	sun	PROPN
fcis-17185	170	8	h	h	PROPN
fcis-17185	170	9	,	,	PUNCT
fcis-17185	170	10	su	su	PROPN
fcis-17185	170	11	y	y	PROPN
fcis-17185	170	12	,	,	PUNCT
fcis-17185	170	13	huang	huang	PROPN
fcis-17185	170	14	.	.	PUNCT
fcis-17185	170	15	muspel	muspel	PROPN
fcis-17185	170	16	-	-	PUNCT
fcis-17185	170	17	fi	fi	NOUN
fcis-17185	170	18	:	:	PUNCT
fcis-17185	170	19	multipath	multipath	PROPN
fcis-17185	170	20	subspace	subspace	PROPN
fcis-17185	170	21	projection	projection	PROPN
fcis-17185	170	22	and	and	CCONJ
fcis-17185	170	23	elm	elm	NOUN
fcis-17185	170	24	-	-	PUNCT
fcis-17185	170	25	based	base	VERB
fcis-17185	170	26	fingerprint	fingerprint	NOUN
fcis-17185	170	27	localization[j	localization[j	PROPN
fcis-17185	170	28	]	]	PUNCT
fcis-17185	170	29	.	.	PUNCT
fcis-17185	171	1	ieee	ieee	PROPN
fcis-17185	171	2	signal	signal	NOUN
fcis-17185	171	3	processing	processing	NOUN
fcis-17185	171	4	letters	letter	NOUN
fcis-17185	171	5	,	,	PUNCT
fcis-17185	171	6	2022	2022	NUM
fcis-17185	171	7	,	,	PUNCT
fcis-17185	171	8	29	29	NUM
fcis-17185	171	9	:	:	SYM
fcis-17185	171	10	329	329	NUM
fcis-17185	171	11	-	-	SYM
fcis-17185	171	12	333	333	NUM
fcis-17185	171	13	.	.	PUNCT
fcis-17185	172	1	[	[	X
fcis-17185	172	2	7	7	X
fcis-17185	172	3	]	]	X
fcis-17185	172	4	zhu	zhu	PROPN
fcis-17185	173	1	q	q	NOUN
fcis-17185	173	2	,	,	PUNCT
fcis-17185	173	3	qin	qin	PROPN
fcis-17185	173	4	a	a	X
fcis-17185	173	5	,	,	PUNCT
fcis-17185	173	6	suganthan	suganthan	PROPN
fcis-17185	173	7	p.	p.	NOUN
fcis-17185	173	8	evolutionary	evolutionary	ADJ
fcis-17185	173	9	extreme	extreme	ADJ
fcis-17185	173	10	learning	learning	PROPN
fcis-17185	173	11	machine[j	machine[j	PROPN
fcis-17185	173	12	]	]	PUNCT
fcis-17185	173	13	.	.	PUNCT
fcis-17185	174	1	pattern	pattern	NOUN
fcis-17185	174	2	recognit,2005	recognit,2005	NOUN
fcis-17185	174	3	,	,	PUNCT
fcis-17185	174	4	38	38	NUM
fcis-17185	174	5	,	,	PUNCT
fcis-17185	174	6	1759	1759	NUM
fcis-17185	174	7	-	-	SYM
fcis-17185	174	8	1763	1763	NUM
fcis-17185	174	9	.	.	PUNCT
fcis-17185	175	1	[	[	X
fcis-17185	175	2	8	8	X
fcis-17185	175	3	]	]	X
fcis-17185	175	4	ling	ling	NOUN
fcis-17185	175	5	q	q	PROPN
fcis-17185	175	6	,	,	PUNCT
fcis-17185	175	7	han	han	PROPN
fcis-17185	175	8	f.	f.	PROPN
fcis-17185	175	9	han	han	PROPN
fcis-17185	175	10	,	,	PUNCT
fcis-17185	175	11	yao	yao	PROPN
fcis-17185	175	12	h.	h.	PROPN
fcis-17185	176	1	an	an	DET
fcis-17185	176	2	improved	improve	VERB
fcis-17185	176	3	evolutionary	evolutionary	ADJ
fcis-17185	176	4	extreme	extreme	ADJ
fcis-17185	176	5	learning	learning	NOUN
fcis-17185	176	6	machine	machine	NOUN
fcis-17185	176	7	based	base	VERB
fcis-17185	176	8	on	on	ADP
fcis-17185	176	9	particle	particle	NOUN
fcis-17185	176	10	swarm	swarm	NOUN
fcis-17185	176	11	optimization	optimization	NOUN
fcis-17185	176	12	[	[	X
fcis-17185	176	13	j	j	X
fcis-17185	176	14	]	]	X
fcis-17185	176	15	.	.	PUNCT
fcis-17185	177	1	neurocomputing	neurocomputing	NOUN
fcis-17185	177	2	,	,	PUNCT
fcis-17185	177	3	2013	2013	NUM
fcis-17185	177	4	,	,	PUNCT
fcis-17185	177	5	116	116	NUM
fcis-17185	177	6	:	:	PUNCT
fcis-17185	177	7	87	87	NUM
fcis-17185	177	8	-	-	SYM
fcis-17185	177	9	93	93	NUM
fcis-17185	177	10	.	.	PUNCT
fcis-17185	178	1	[	[	X
fcis-17185	178	2	9	9	NUM
fcis-17185	178	3	]	]	X
fcis-17185	178	4	liu	liu	PROPN
fcis-17185	178	5	t	t	PROPN
fcis-17185	178	6	,	,	PUNCT
fcis-17185	178	7	fan	fan	PROPN
fcis-17185	178	8	q	q	PROPN
fcis-17185	178	9	,	,	PUNCT
fcis-17185	178	10	kang	kang	PROPN
fcis-17185	178	11	q	q	PROPN
fcis-17185	178	12	,	,	PUNCT
fcis-17185	178	13	niu	niu	PROPN
fcis-17185	178	14	l.	l.	PROPN
fcis-17185	178	15	extreme	extreme	PROPN
fcis-17185	178	16	learning	learning	PROPN
fcis-17185	178	17	machine	machine	NOUN
fcis-17185	178	18	based	base	VERB
fcis-17185	178	19	on	on	ADP
fcis-17185	178	20	firefly	firefly	PROPN
fcis-17185	178	21	adaptive	adaptive	ADJ
fcis-17185	178	22	flower	flower	NOUN
fcis-17185	178	23	pollination	pollination	NOUN
fcis-17185	178	24	algorithm	algorithm	NOUN
fcis-17185	178	25	optimization	optimization	NOUN
fcis-17185	179	1	[	[	X
fcis-17185	179	2	j	j	X
fcis-17185	179	3	]	]	X
fcis-17185	179	4	.	.	PUNCT
fcis-17185	180	1	processes	process	NOUN
fcis-17185	180	2	,	,	PUNCT
fcis-17185	180	3	2020	2020	NUM
fcis-17185	180	4	,	,	PUNCT
fcis-17185	180	5	8(12	8(12	NUM
fcis-17185	180	6	):	):	PUNCT
fcis-17185	180	7	1583	1583	NUM
fcis-17185	180	8	.	.	PUNCT
fcis-17185	181	1	[	[	X
fcis-17185	181	2	10	10	NUM
fcis-17185	181	3	]	]	PUNCT
fcis-17185	181	4	derya	derya	NOUN
fcis-17185	181	5	a	a	PRON
fcis-17185	181	6	,	,	PUNCT
fcis-17185	181	7	akif	akif	PROPN
fcis-17185	181	8	d.	d.	PROPN
fcis-17185	181	9	an	an	DET
fcis-17185	181	10	expert	expert	NOUN
fcis-17185	181	11	diagnosis	diagnosis	NOUN
fcis-17185	181	12	system	system	NOUN
fcis-17185	181	13	for	for	ADP
fcis-17185	181	14	parkinson	parkinson	NOUN
fcis-17185	181	15	disease	disease	NOUN
fcis-17185	181	16	based	base	VERB
fcis-17185	181	17	on	on	ADP
fcis-17185	181	18	genetic	genetic	ADJ
fcis-17185	181	19	algorithm	algorithm	NOUN
fcis-17185	181	20	-	-	PUNCT
fcis-17185	181	21	wavelet	wavelet	NOUN
fcis-17185	181	22	kernel	kernel	NOUN
fcis-17185	181	23	-	-	PUNCT
fcis-17185	181	24	extreme	extreme	ADJ
fcis-17185	181	25	learning	learning	NOUN
fcis-17185	181	26	machine[j	machine[j	PROPN
fcis-17185	181	27	]	]	PUNCT
fcis-17185	181	28	.	.	PUNCT
fcis-17185	182	1	parkinson	parkinson	NOUN
fcis-17185	182	2	’s	’s	PART
fcis-17185	182	3	disease	disease	NOUN
fcis-17185	182	4	,	,	PUNCT
fcis-17185	182	5	2016	2016	NUM
fcis-17185	182	6	,	,	PUNCT
fcis-17185	182	7	1	1	NUM
fcis-17185	182	8	-	-	SYM
fcis-17185	182	9	9	9	NUM
fcis-17185	182	10	.	.	PUNCT
fcis-17185	183	1	[	[	X
fcis-17185	183	2	11	11	NUM
fcis-17185	183	3	]	]	X
fcis-17185	183	4	mirjalili	mirjalili	NOUN
fcis-17185	183	5	s	s	NOUN
fcis-17185	183	6	,	,	PUNCT
fcis-17185	183	7	gandomi	gandomi	VERB
fcis-17185	183	8	a	a	DET
fcis-17185	183	9	h	h	NOUN
fcis-17185	183	10	,	,	PUNCT
fcis-17185	183	11	mirjalili	mirjalili	NOUN
fcis-17185	183	12	s	s	PART
fcis-17185	183	13	z	z	PROPN
fcis-17185	183	14	,	,	PUNCT
fcis-17185	183	15	et	et	PROPN
fcis-17185	183	16	al	al	PROPN
fcis-17185	183	17	.	.	PUNCT
fcis-17185	184	1	salp	salp	PROPN
fcis-17185	184	2	swarm	swarm	NOUN
fcis-17185	184	3	algorithm	algorithm	NOUN
fcis-17185	184	4	:	:	PUNCT
fcis-17185	184	5	a	a	DET
fcis-17185	184	6	bio	bio	ADJ
fcis-17185	184	7	-	-	PUNCT
fcis-17185	184	8	inspired	inspire	VERB
fcis-17185	184	9	optimizer	optimizer	NOUN
fcis-17185	184	10	for	for	ADP
fcis-17185	184	11	engineering	engineering	NOUN
fcis-17185	184	12	design	design	NOUN
fcis-17185	184	13	problems[j	problems[j	NOUN
fcis-17185	184	14	]	]	PUNCT
fcis-17185	184	15	.	.	PUNCT
fcis-17185	185	1	advances	advance	NOUN
fcis-17185	185	2	in	in	ADP
fcis-17185	185	3	engineering	engineering	NOUN
fcis-17185	185	4	software	software	NOUN
fcis-17185	185	5	,	,	PUNCT
fcis-17185	185	6	2017	2017	NUM
fcis-17185	185	7	,	,	PUNCT
fcis-17185	185	8	114	114	NUM
fcis-17185	185	9	:	:	SYM
fcis-17185	185	10	163	163	NUM
fcis-17185	185	11	-	-	SYM
fcis-17185	185	12	191	191	NUM
fcis-17185	185	13	.	.	PUNCT
fcis-17185	186	1	[	[	X
fcis-17185	186	2	12	12	NUM
fcis-17185	186	3	]	]	X
fcis-17185	186	4	tu	tu	PROPN
fcis-17185	186	5	q	q	PROPN
fcis-17185	186	6	,	,	PUNCT
fcis-17185	186	7	liu	liu	PROPN
fcis-17185	186	8	x	x	PROPN
fcis-17185	186	9	,	,	PUNCT
fcis-17185	186	10	xie	xie	PROPN
fcis-17185	186	11	y	y	PROPN
fcis-17185	186	12	,	,	PUNCT
fcis-17185	186	13	han	han	PROPN
fcis-17185	186	14	g.	g.	PROPN
fcis-17185	186	15	range	range	NOUN
fcis-17185	186	16	-	-	PUNCT
fcis-17185	186	17	free	free	ADJ
fcis-17185	186	18	localization	localization	NOUN
fcis-17185	186	19	using	use	VERB
fcis-17185	186	20	extreme	extreme	ADJ
fcis-17185	186	21	learning	learn	VERB
fcis-17185	186	22	machine	machine	NOUN
fcis-17185	186	23	and	and	CCONJ
fcis-17185	186	24	ring	ring	NOUN
fcis-17185	186	25	-	-	PUNCT
fcis-17185	186	26	shaped	shape	VERB
fcis-17185	186	27	salp	salp	NOUN
fcis-17185	186	28	swarm	swarm	NOUN
fcis-17185	186	29	algorithm	algorithm	NOUN
fcis-17185	186	30	in	in	ADP
fcis-17185	186	31	anisotropic	anisotropic	NOUN
fcis-17185	186	32	networks[j	networks[j	PROPN
fcis-17185	186	33	]	]	PUNCT
fcis-17185	186	34	.	.	PUNCT
fcis-17185	187	1	ieee	ieee	PROPN
fcis-17185	187	2	internet	internet	NOUN
fcis-17185	187	3	of	of	ADP
fcis-17185	187	4	things	thing	NOUN
fcis-17185	187	5	journal	journal	NOUN
fcis-17185	187	6	,	,	PUNCT
fcis-17185	187	7	2022,10(9	2022,10(9	PROPN
fcis-17185	187	8	):	):	PUNCT
fcis-17185	187	9	8228	8228	NUM
fcis-17185	187	10	.	.	PUNCT