id	sid	tid	token	lemma	pos
ap-296	1	1	ap01_6.vp	ap01_6.vp	ADJ
ap-296	1	2	1	1	NUM
ap-296	1	3	introduction	introduction	NOUN
ap-296	1	4	a	a	DET
ap-296	1	5	fuzzy	fuzzy	ADJ
ap-296	1	6	logic	logic	NOUN
ap-296	1	7	neural	neural	ADJ
ap-296	1	8	network	network	NOUN
ap-296	1	9	(	(	PUNCT
ap-296	1	10	flnn	flnn	NOUN
ap-296	1	11	)	)	PUNCT
ap-296	2	1	[	[	X
ap-296	2	2	10	10	NUM
ap-296	2	3	]	]	PUNCT
ap-296	2	4	is	be	AUX
ap-296	2	5	a	a	DET
ap-296	2	6	general	general	ADJ
ap-296	2	7	nonlinear	nonlinear	ADJ
ap-296	2	8	interpolator	interpolator	NOUN
ap-296	2	9	used	use	VERB
ap-296	2	10	for	for	ADP
ap-296	2	11	modeling	model	VERB
ap-296	2	12	static	static	ADJ
ap-296	2	13	systems	system	NOUN
ap-296	2	14	.	.	PUNCT
ap-296	3	1	the	the	DET
ap-296	3	2	structure	structure	NOUN
ap-296	3	3	and	and	CCONJ
ap-296	3	4	rough	rough	ADJ
ap-296	3	5	setting	setting	NOUN
ap-296	3	6	of	of	ADP
ap-296	3	7	flnn	flnn	NOUN
ap-296	3	8	parameters	parameter	NOUN
ap-296	3	9	is	be	AUX
ap-296	3	10	usually	usually	ADV
ap-296	3	11	done	do	VERB
ap-296	3	12	manually	manually	ADV
ap-296	3	13	by	by	ADP
ap-296	3	14	an	an	DET
ap-296	3	15	expert	expert	NOUN
ap-296	3	16	.	.	PUNCT
ap-296	4	1	fine	fine	ADJ
ap-296	4	2	-	-	PUNCT
ap-296	4	3	tuning	tuning	NOUN
ap-296	4	4	is	be	AUX
ap-296	4	5	done	do	VERB
ap-296	4	6	by	by	ADP
ap-296	4	7	numerical	numerical	ADJ
ap-296	4	8	optimization	optimization	NOUN
ap-296	4	9	techniques	technique	NOUN
ap-296	4	10	using	use	VERB
ap-296	4	11	reference	reference	NOUN
ap-296	4	12	input	input	NOUN
ap-296	4	13	-	-	PUNCT
ap-296	4	14	output	output	NOUN
ap-296	4	15	data	datum	NOUN
ap-296	4	16	.	.	PUNCT
ap-296	5	1	the	the	DET
ap-296	5	2	use	use	NOUN
ap-296	5	3	of	of	ADP
ap-296	5	4	a	a	DET
ap-296	5	5	random	random	ADJ
ap-296	5	6	search	search	NOUN
ap-296	5	7	technique	technique	NOUN
ap-296	5	8	is	be	AUX
ap-296	5	9	an	an	DET
ap-296	5	10	option	option	NOUN
ap-296	5	11	for	for	ADP
ap-296	5	12	solving	solve	VERB
ap-296	5	13	the	the	DET
ap-296	5	14	problem	problem	NOUN
ap-296	5	15	.	.	PUNCT
ap-296	6	1	genetic	genetic	ADJ
ap-296	6	2	algorithm	algorithm	NOUN
ap-296	6	3	(	(	PUNCT
ap-296	6	4	ga	ga	NOUN
ap-296	6	5	)	)	PUNCT
ap-296	6	6	is	be	AUX
ap-296	6	7	an	an	DET
ap-296	6	8	evolutionary	evolutionary	ADJ
ap-296	6	9	method	method	NOUN
ap-296	6	10	that	that	PRON
ap-296	6	11	simulates	simulate	VERB
ap-296	6	12	the	the	DET
ap-296	6	13	process	process	NOUN
ap-296	6	14	of	of	ADP
ap-296	6	15	natural	natural	ADJ
ap-296	6	16	selection	selection	NOUN
ap-296	6	17	and	and	CCONJ
ap-296	6	18	survival	survival	NOUN
ap-296	6	19	of	of	ADP
ap-296	6	20	the	the	DET
ap-296	6	21	fittest	fit	ADJ
ap-296	6	22	.	.	PUNCT
ap-296	7	1	gas	gas	NOUN
ap-296	7	2	randomly	randomly	ADV
ap-296	7	3	generate	generate	VERB
ap-296	7	4	a	a	DET
ap-296	7	5	set	set	NOUN
ap-296	7	6	of	of	ADP
ap-296	7	7	potential	potential	ADJ
ap-296	7	8	problem	problem	NOUN
ap-296	7	9	solutions	solution	NOUN
ap-296	7	10	,	,	PUNCT
ap-296	7	11	and	and	CCONJ
ap-296	7	12	manipulate	manipulate	VERB
ap-296	7	13	them	they	PRON
ap-296	7	14	using	use	VERB
ap-296	7	15	genetic	genetic	ADJ
ap-296	7	16	operators	operator	NOUN
ap-296	7	17	.	.	PUNCT
ap-296	8	1	each	each	DET
ap-296	8	2	solution	solution	NOUN
ap-296	8	3	is	be	AUX
ap-296	8	4	assigned	assign	VERB
ap-296	8	5	a	a	DET
ap-296	8	6	scalar	scalar	ADJ
ap-296	8	7	fitness	fitness	NOUN
ap-296	8	8	value	value	NOUN
ap-296	8	9	,	,	PUNCT
ap-296	8	10	which	which	PRON
ap-296	8	11	is	be	AUX
ap-296	8	12	a	a	DET
ap-296	8	13	numerical	numerical	ADJ
ap-296	8	14	assessment	assessment	NOUN
ap-296	8	15	of	of	ADP
ap-296	8	16	how	how	SCONJ
ap-296	8	17	well	well	ADV
ap-296	8	18	it	it	PRON
ap-296	8	19	solves	solve	VERB
ap-296	8	20	the	the	DET
ap-296	8	21	problem	problem	NOUN
ap-296	8	22	.	.	PUNCT
ap-296	9	1	through	through	ADP
ap-296	9	2	crossover	crossover	NOUN
ap-296	9	3	and	and	CCONJ
ap-296	9	4	mutation	mutation	NOUN
ap-296	9	5	operations	operation	NOUN
ap-296	9	6	new	new	ADJ
ap-296	9	7	feasible	feasible	ADJ
ap-296	9	8	solutions	solution	NOUN
ap-296	9	9	are	be	AUX
ap-296	9	10	hopefully	hopefully	ADV
ap-296	9	11	generated	generate	VERB
ap-296	9	12	.	.	PUNCT
ap-296	10	1	the	the	DET
ap-296	10	2	process	process	NOUN
ap-296	10	3	continues	continue	VERB
ap-296	10	4	until	until	SCONJ
ap-296	10	5	the	the	DET
ap-296	10	6	termination	termination	NOUN
ap-296	10	7	condition	condition	NOUN
ap-296	10	8	is	be	AUX
ap-296	10	9	met	meet	VERB
ap-296	10	10	.	.	PUNCT
ap-296	11	1	further	further	ADJ
ap-296	11	2	discussion	discussion	NOUN
ap-296	11	3	on	on	ADP
ap-296	11	4	gas	gas	NOUN
ap-296	11	5	can	can	AUX
ap-296	11	6	be	be	AUX
ap-296	11	7	found	find	VERB
ap-296	11	8	in	in	ADP
ap-296	11	9	[	[	X
ap-296	11	10	6	6	NUM
ap-296	11	11	]	]	PUNCT
ap-296	11	12	,	,	PUNCT
ap-296	11	13	[	[	X
ap-296	11	14	17	17	NUM
ap-296	11	15	]	]	PUNCT
ap-296	11	16	,	,	PUNCT
ap-296	11	17	and	and	CCONJ
ap-296	11	18	[	[	X
ap-296	11	19	5	5	NUM
ap-296	11	20	]	]	PUNCT
ap-296	11	21	.	.	PUNCT
ap-296	12	1	when	when	SCONJ
ap-296	12	2	ga	ga	PROPN
ap-296	12	3	is	be	AUX
ap-296	12	4	implemented	implement	VERB
ap-296	12	5	as	as	ADP
ap-296	12	6	a	a	DET
ap-296	12	7	learning	learning	NOUN
ap-296	12	8	procedure	procedure	NOUN
ap-296	12	9	,	,	PUNCT
ap-296	12	10	the	the	DET
ap-296	12	11	flnn	flnn	NOUN
ap-296	12	12	parameters	parameter	NOUN
ap-296	12	13	are	be	AUX
ap-296	12	14	coded	code	VERB
ap-296	12	15	to	to	PART
ap-296	12	16	form	form	VERB
ap-296	12	17	a	a	DET
ap-296	12	18	string	string	NOUN
ap-296	12	19	referred	refer	VERB
ap-296	12	20	to	to	ADP
ap-296	12	21	as	as	ADP
ap-296	12	22	a	a	DET
ap-296	12	23	chromosome	chromosome	NOUN
ap-296	12	24	.	.	PUNCT
ap-296	13	1	under	under	ADP
ap-296	13	2	instances	instance	NOUN
ap-296	13	3	in	in	ADP
ap-296	13	4	the	the	DET
ap-296	13	5	population	population	NOUN
ap-296	13	6	of	of	ADP
ap-296	13	7	chromosomes	chromosome	NOUN
ap-296	13	8	,	,	PUNCT
ap-296	13	9	the	the	DET
ap-296	13	10	genetic	genetic	ADJ
ap-296	13	11	operations	operation	NOUN
ap-296	13	12	are	be	AUX
ap-296	13	13	performed	perform	VERB
ap-296	13	14	.	.	PUNCT
ap-296	14	1	the	the	DET
ap-296	14	2	fitness	fitness	NOUN
ap-296	14	3	is	be	AUX
ap-296	14	4	inversely	inversely	ADV
ap-296	14	5	proportional	proportional	ADJ
ap-296	14	6	to	to	ADP
ap-296	14	7	the	the	DET
ap-296	14	8	whole	whole	ADJ
ap-296	14	9	system	system	NOUN
ap-296	14	10	error	error	NOUN
ap-296	14	11	,	,	PUNCT
ap-296	14	12	which	which	PRON
ap-296	14	13	represents	represent	VERB
ap-296	14	14	the	the	DET
ap-296	14	15	difference	difference	NOUN
ap-296	14	16	between	between	ADP
ap-296	14	17	the	the	DET
ap-296	14	18	required	require	VERB
ap-296	14	19	and	and	CCONJ
ap-296	14	20	actual	actual	ADJ
ap-296	14	21	network	network	NOUN
ap-296	14	22	response	response	NOUN
ap-296	14	23	.	.	PUNCT
ap-296	15	1	the	the	DET
ap-296	15	2	remainder	remainder	NOUN
ap-296	15	3	of	of	ADP
ap-296	15	4	this	this	DET
ap-296	15	5	paper	paper	NOUN
ap-296	15	6	is	be	AUX
ap-296	15	7	organized	organize	VERB
ap-296	15	8	as	as	SCONJ
ap-296	15	9	follows	follow	VERB
ap-296	15	10	:	:	PUNCT
ap-296	15	11	section	section	NOUN
ap-296	15	12	2	2	NUM
ap-296	15	13	illustrates	illustrate	VERB
ap-296	15	14	the	the	DET
ap-296	15	15	structure	structure	NOUN
ap-296	15	16	of	of	ADP
ap-296	15	17	a	a	DET
ap-296	15	18	fuzzy	fuzzy	ADJ
ap-296	15	19	logic	logic	NOUN
ap-296	15	20	neural	neural	ADJ
ap-296	15	21	network	network	NOUN
ap-296	15	22	model	model	NOUN
ap-296	15	23	.	.	PUNCT
ap-296	16	1	in	in	ADP
ap-296	16	2	section	section	NOUN
ap-296	16	3	3	3	NUM
ap-296	16	4	,	,	PUNCT
ap-296	16	5	the	the	DET
ap-296	16	6	derivation	derivation	NOUN
ap-296	16	7	of	of	ADP
ap-296	16	8	the	the	DET
ap-296	16	9	membership	membership	NOUN
ap-296	16	10	function	function	NOUN
ap-296	16	11	(	(	PUNCT
ap-296	16	12	mf	mf	NOUN
ap-296	16	13	)	)	PUNCT
ap-296	16	14	constraints	constraint	NOUN
ap-296	16	15	is	be	AUX
ap-296	16	16	performed	perform	VERB
ap-296	16	17	for	for	ADP
ap-296	16	18	mfs	mfs	NOUN
ap-296	16	19	of	of	ADP
ap-296	16	20	gaussian	gaussian	ADJ
ap-296	16	21	shape	shape	NOUN
ap-296	16	22	.	.	PUNCT
ap-296	17	1	section	section	NOUN
ap-296	17	2	4	4	NUM
ap-296	17	3	shows	show	VERB
ap-296	17	4	the	the	DET
ap-296	17	5	laga	laga	NOUN
ap-296	17	6	approach	approach	NOUN
ap-296	17	7	.	.	PUNCT
ap-296	18	1	the	the	DET
ap-296	18	2	proposed	propose	VERB
ap-296	18	3	genetic	genetic	ADJ
ap-296	18	4	algorithm	algorithm	NOUN
ap-296	18	5	with	with	ADP
ap-296	18	6	constrained	constrain	VERB
ap-296	18	7	search	search	NOUN
ap-296	18	8	space	space	NOUN
ap-296	18	9	is	be	AUX
ap-296	18	10	explained	explain	VERB
ap-296	18	11	in	in	ADP
ap-296	18	12	detail	detail	NOUN
ap-296	18	13	in	in	ADP
ap-296	18	14	section	section	NOUN
ap-296	18	15	5	5	NUM
ap-296	18	16	.	.	PUNCT
ap-296	19	1	the	the	DET
ap-296	19	2	explanation	explanation	NOUN
ap-296	19	3	of	of	ADP
ap-296	19	4	the	the	DET
ap-296	19	5	optimization	optimization	NOUN
ap-296	19	6	method	method	NOUN
ap-296	19	7	is	be	AUX
ap-296	19	8	presented	present	VERB
ap-296	19	9	in	in	ADP
ap-296	19	10	detail	detail	NOUN
ap-296	19	11	on	on	ADP
ap-296	19	12	the	the	DET
ap-296	19	13	basis	basis	NOUN
ap-296	19	14	of	of	ADP
ap-296	19	15	two	two	NUM
ap-296	19	16	application	application	NOUN
ap-296	19	17	examples	example	NOUN
ap-296	19	18	,	,	PUNCT
ap-296	19	19	and	and	CCONJ
ap-296	19	20	the	the	DET
ap-296	19	21	conclusion	conclusion	NOUN
ap-296	19	22	is	be	AUX
ap-296	19	23	presented	present	VERB
ap-296	19	24	in	in	ADP
ap-296	19	25	section	section	NOUN
ap-296	19	26	6	6	NUM
ap-296	19	27	.	.	SYM
ap-296	19	28	2	2	NUM
ap-296	19	29	proposed	propose	VERB
ap-296	19	30	fuzzy	fuzzy	ADJ
ap-296	19	31	logic	logic	NOUN
ap-296	19	32	neural	neural	ADJ
ap-296	19	33	network	network	NOUN
ap-296	19	34	(	(	PUNCT
ap-296	19	35	flnn	flnn	NOUN
ap-296	19	36	)	)	PUNCT
ap-296	19	37	the	the	DET
ap-296	19	38	flnn	flnn	NOUN
ap-296	19	39	model	model	NOUN
ap-296	19	40	as	as	SCONJ
ap-296	19	41	a	a	DET
ap-296	19	42	general	general	ADJ
ap-296	19	43	nonlinear	nonlinear	ADJ
ap-296	19	44	interpolator	interpolator	NOUN
ap-296	19	45	is	be	AUX
ap-296	19	46	built	build	VERB
ap-296	19	47	using	use	VERB
ap-296	19	48	the	the	DET
ap-296	19	49	multilayer	multilayer	ADJ
ap-296	19	50	fuzzy	fuzzy	ADJ
ap-296	19	51	logic	logic	NOUN
ap-296	19	52	neural	neural	ADJ
ap-296	19	53	network	network	NOUN
ap-296	19	54	shown	show	VERB
ap-296	19	55	in	in	ADP
ap-296	19	56	fig	fig	NOUN
ap-296	19	57	.	.	PUNCT
ap-296	20	1	1	1	NUM
ap-296	20	2	,	,	PUNCT
ap-296	20	3	proposed	propose	VERB
ap-296	20	4	by	by	ADP
ap-296	20	5	lin	lin	PROPN
ap-296	21	1	[	[	X
ap-296	21	2	10	10	NUM
ap-296	21	3	]	]	PUNCT
ap-296	21	4	,	,	PUNCT
ap-296	21	5	with	with	ADP
ap-296	21	6	some	some	DET
ap-296	21	7	modification	modification	NOUN
ap-296	21	8	by	by	ADP
ap-296	21	9	[	[	X
ap-296	21	10	9	9	NUM
ap-296	21	11	]	]	PUNCT
ap-296	21	12	.	.	PUNCT
ap-296	22	1	this	this	PRON
ap-296	22	2	is	be	AUX
ap-296	22	3	a	a	DET
ap-296	22	4	particular	particular	ADJ
ap-296	22	5	implementation	implementation	NOUN
ap-296	22	6	of	of	ADP
ap-296	22	7	a	a	DET
ap-296	22	8	fuzzy	fuzzy	ADJ
ap-296	22	9	system	system	NOUN
ap-296	22	10	equipped	equip	VERB
ap-296	22	11	with	with	ADP
ap-296	22	12	fuzzification	fuzzification	NOUN
ap-296	22	13	and	and	CCONJ
ap-296	22	14	defuzzification	defuzzification	NOUN
ap-296	22	15	interfaces	interface	NOUN
ap-296	22	16	.	.	PUNCT
ap-296	23	1	this	this	DET
ap-296	23	2	network	network	NOUN
ap-296	23	3	represents	represent	VERB
ap-296	23	4	a	a	DET
ap-296	23	5	linguistic	linguistic	ADJ
ap-296	23	6	fuzzy	fuzzy	ADJ
ap-296	23	7	system	system	NOUN
ap-296	23	8	with	with	ADP
ap-296	23	9	a	a	DET
ap-296	23	10	general	general	ADJ
ap-296	23	11	rule	rule	NOUN
ap-296	23	12	-	-	PUNCT
ap-296	23	13	based	base	VERB
ap-296	23	14	structure	structure	NOUN
ap-296	23	15	.	.	PUNCT
ap-296	24	1	the	the	DET
ap-296	24	2	following	follow	VERB
ap-296	24	3	example	example	NOUN
ap-296	24	4	demonstrates	demonstrate	VERB
ap-296	24	5	this	this	DET
ap-296	24	6	structure	structure	NOUN
ap-296	24	7	:	:	PUNCT
ap-296	24	8	©	©	PROPN
ap-296	24	9	czech	czech	PROPN
ap-296	24	10	technical	technical	PROPN
ap-296	24	11	university	university	PROPN
ap-296	24	12	publishing	publishing	NOUN
ap-296	24	13	house	house	NOUN
ap-296	24	14	http://ctn.cvut.cz/ap/	http://ctn.cvut.cz/ap/	PROPN
ap-296	24	15	69	69	NUM
ap-296	24	16	acta	acta	PROPN
ap-296	24	17	polytechnica	polytechnica	PROPN
ap-296	24	18	vol	vol	NOUN
ap-296	24	19	.	.	PUNCT
ap-296	25	1	41	41	NUM
ap-296	25	2	no	no	NOUN
ap-296	25	3	.	.	PUNCT
ap-296	26	1	6/2001	6/2001	NUM
ap-296	26	2	modeling	model	VERB
ap-296	26	3	nonlinear	nonlinear	ADJ
ap-296	26	4	systems	system	NOUN
ap-296	26	5	by	by	ADP
ap-296	26	6	a	a	DET
ap-296	26	7	fuzzy	fuzzy	ADJ
ap-296	26	8	logic	logic	NOUN
ap-296	26	9	neural	neural	ADJ
ap-296	26	10	network	network	NOUN
ap-296	26	11	using	use	VERB
ap-296	26	12	genetic	genetic	ADJ
ap-296	26	13	algorithms	algorithm	NOUN
ap-296	26	14	abdel	abdel	PROPN
ap-296	26	15	-	-	PUNCT
ap-296	26	16	fattah	fattah	PROPN
ap-296	26	17	attia	attia	NOUN
ap-296	26	18	,	,	PUNCT
ap-296	26	19	p.	p.	PROPN
ap-296	26	20	horáček	horáček	VERB
ap-296	26	21	the	the	DET
ap-296	26	22	main	main	ADJ
ap-296	26	23	aim	aim	NOUN
ap-296	26	24	of	of	ADP
ap-296	26	25	this	this	DET
ap-296	26	26	work	work	NOUN
ap-296	26	27	is	be	AUX
ap-296	26	28	to	to	PART
ap-296	26	29	optimize	optimize	VERB
ap-296	26	30	the	the	DET
ap-296	26	31	parameters	parameter	NOUN
ap-296	26	32	of	of	ADP
ap-296	26	33	the	the	DET
ap-296	26	34	constrained	constrain	VERB
ap-296	26	35	membership	membership	NOUN
ap-296	26	36	function	function	NOUN
ap-296	26	37	of	of	ADP
ap-296	26	38	the	the	DET
ap-296	26	39	fuzzy	fuzzy	ADJ
ap-296	26	40	logic	logic	NOUN
ap-296	26	41	neural	neural	ADJ
ap-296	26	42	network	network	NOUN
ap-296	26	43	(	(	PUNCT
ap-296	26	44	flnn	flnn	NOUN
ap-296	26	45	)	)	PUNCT
ap-296	26	46	.	.	PUNCT
ap-296	27	1	the	the	DET
ap-296	27	2	constraints	constraint	NOUN
ap-296	27	3	may	may	AUX
ap-296	27	4	be	be	AUX
ap-296	27	5	an	an	DET
ap-296	27	6	indirect	indirect	ADJ
ap-296	27	7	definition	definition	NOUN
ap-296	27	8	of	of	ADP
ap-296	27	9	the	the	DET
ap-296	27	10	search	search	NOUN
ap-296	27	11	ranges	range	VERB
ap-296	27	12	for	for	ADP
ap-296	27	13	every	every	DET
ap-296	27	14	membership	membership	NOUN
ap-296	27	15	shape	shape	NOUN
ap-296	27	16	forming	form	VERB
ap-296	27	17	parameter	parameter	NOUN
ap-296	27	18	based	base	VERB
ap-296	27	19	on	on	ADP
ap-296	27	20	2nd	2nd	ADJ
ap-296	27	21	order	order	NOUN
ap-296	27	22	fuzzy	fuzzy	ADJ
ap-296	27	23	set	set	VERB
ap-296	27	24	specifications	specification	NOUN
ap-296	27	25	.	.	PUNCT
ap-296	28	1	a	a	DET
ap-296	28	2	particular	particular	ADJ
ap-296	28	3	method	method	NOUN
ap-296	28	4	widely	widely	ADV
ap-296	28	5	applicable	applicable	ADJ
ap-296	28	6	in	in	ADP
ap-296	28	7	solving	solve	VERB
ap-296	28	8	global	global	ADJ
ap-296	28	9	optimization	optimization	NOUN
ap-296	28	10	problems	problem	NOUN
ap-296	28	11	is	be	AUX
ap-296	28	12	introduced	introduce	VERB
ap-296	28	13	.	.	PUNCT
ap-296	29	1	this	this	DET
ap-296	29	2	approach	approach	NOUN
ap-296	29	3	uses	use	VERB
ap-296	29	4	a	a	DET
ap-296	29	5	linear	linear	ADJ
ap-296	29	6	adapted	adapt	VERB
ap-296	29	7	genetic	genetic	ADJ
ap-296	29	8	algorithm	algorithm	NOUN
ap-296	29	9	(	(	PUNCT
ap-296	29	10	laga	laga	NOUN
ap-296	29	11	)	)	PUNCT
ap-296	29	12	to	to	PART
ap-296	29	13	optimize	optimize	VERB
ap-296	29	14	the	the	DET
ap-296	29	15	flnn	flnn	NOUN
ap-296	29	16	parameters	parameter	NOUN
ap-296	29	17	.	.	PUNCT
ap-296	30	1	in	in	ADP
ap-296	30	2	this	this	DET
ap-296	30	3	paper	paper	NOUN
ap-296	30	4	the	the	DET
ap-296	30	5	derivation	derivation	NOUN
ap-296	30	6	of	of	ADP
ap-296	30	7	a	a	DET
ap-296	30	8	2nd	2nd	ADJ
ap-296	30	9	order	order	NOUN
ap-296	30	10	fuzzy	fuzzy	ADJ
ap-296	30	11	set	set	NOUN
ap-296	30	12	is	be	AUX
ap-296	30	13	performed	perform	VERB
ap-296	30	14	for	for	ADP
ap-296	30	15	a	a	DET
ap-296	30	16	membership	membership	NOUN
ap-296	30	17	function	function	NOUN
ap-296	30	18	of	of	ADP
ap-296	30	19	gaussian	gaussian	ADJ
ap-296	30	20	shape	shape	NOUN
ap-296	30	21	,	,	PUNCT
ap-296	30	22	which	which	PRON
ap-296	30	23	is	be	AUX
ap-296	30	24	assumed	assume	VERB
ap-296	30	25	for	for	ADP
ap-296	30	26	the	the	DET
ap-296	30	27	neuro	neuro	NOUN
ap-296	30	28	-	-	PUNCT
ap-296	30	29	fuzzy	fuzzy	ADJ
ap-296	30	30	approach	approach	NOUN
ap-296	30	31	.	.	PUNCT
ap-296	31	1	the	the	DET
ap-296	31	2	explanation	explanation	NOUN
ap-296	31	3	of	of	ADP
ap-296	31	4	the	the	DET
ap-296	31	5	optimization	optimization	NOUN
ap-296	31	6	method	method	NOUN
ap-296	31	7	is	be	AUX
ap-296	31	8	presented	present	VERB
ap-296	31	9	in	in	ADP
ap-296	31	10	detail	detail	NOUN
ap-296	31	11	on	on	ADP
ap-296	31	12	the	the	DET
ap-296	31	13	basis	basis	NOUN
ap-296	31	14	of	of	ADP
ap-296	31	15	two	two	NUM
ap-296	31	16	examples	example	NOUN
ap-296	31	17	.	.	PUNCT
ap-296	32	1	keywords	keyword	NOUN
ap-296	32	2	:	:	PUNCT
ap-296	32	3	genetic	genetic	ADJ
ap-296	32	4	algorithms	algorithm	NOUN
ap-296	32	5	,	,	PUNCT
ap-296	32	6	fuzzy	fuzzy	ADJ
ap-296	32	7	logic	logic	NOUN
ap-296	32	8	neural	neural	ADJ
ap-296	32	9	network	network	NOUN
ap-296	32	10	,	,	PUNCT
ap-296	32	11	2nd	2nd	ADJ
ap-296	32	12	order	order	NOUN
ap-296	32	13	fuzzy	fuzzy	ADJ
ap-296	32	14	sets	set	NOUN
ap-296	32	15	.	.	PUNCT
ap-296	33	1	and	and	CCONJ
ap-296	34	1	and	and	CCONJ
ap-296	34	2	and	and	CCONJ
ap-296	34	3	and	and	CCONJ
ap-296	34	4	and	and	CCONJ
ap-296	34	5	and	and	CCONJ
ap-296	34	6	w	w	PROPN
ap-296	34	7	w	w	PROPN
ap-296	34	8	w	w	PROPN
ap-296	34	9	w	w	PROPN
ap-296	34	10	w	w	PROPN
ap-296	34	11	w	w	PROPN
ap-296	34	12	1	1	NUM
ap-296	34	13	1a	1a	NUM
ap-296	34	14	2	2	NUM
ap-296	34	15	1a	1a	X
ap-296	34	16	3	3	NUM
ap-296	34	17	1a	1a	NOUN
ap-296	34	18	1	1	NUM
ap-296	34	19	na	na	PART
ap-296	34	20	2	2	NUM
ap-296	34	21	na	na	PART
ap-296	34	22	3	3	NUM
ap-296	34	23	na	na	NOUN
ap-296	34	24	.	.	PUNCT
ap-296	34	25	.	.	PUNCT
ap-296	35	1	.	.	PUNCT
ap-296	35	2	.	.	PUNCT
ap-296	36	1	.	.	PUNCT
ap-296	36	2	.	.	PUNCT
ap-296	37	1	.	.	PUNCT
ap-296	38	1	1x	1x	PROPN
ap-296	38	2	nx	nx	PROPN
ap-296	38	3	3	3	NUM
ap-296	38	4	nb	nb	PROPN
ap-296	38	5	2	2	NUM
ap-296	38	6	nb	nb	PROPN
ap-296	38	7	1	1	NUM
ap-296	38	8	nb	nb	PROPN
ap-296	38	9	3	3	NUM
ap-296	38	10	1b	1b	NUM
ap-296	38	11	2	2	NUM
ap-296	38	12	1b	1b	NUM
ap-296	38	13	1	1	NUM
ap-296	38	14	1b	1b	NUM
ap-296	38	15	inputs	input	NOUN
ap-296	38	16	premise	premise	ADJ
ap-296	38	17	comparison	comparison	NOUN
ap-296	38	18	and	and	CCONJ
ap-296	38	19	y	y	PROPN
ap-296	38	20	rule	rule	VERB
ap-296	38	21	weights	weight	NOUN
ap-296	38	22	rule	rule	NOUN
ap-296	38	23	consequent	consequent	ADJ
ap-296	38	24	aggregation	aggregation	NOUN
ap-296	38	25	output	output	NOUN
ap-296	38	26	fig	fig	NOUN
ap-296	38	27	.	.	PUNCT
ap-296	39	1	1	1	NUM
ap-296	39	2	:	:	PUNCT
ap-296	39	3	fuzzy	fuzzy	ADJ
ap-296	39	4	logic	logic	NOUN
ap-296	39	5	neural	neural	ADJ
ap-296	39	6	network	network	NOUN
ap-296	39	7	topology	topology	NOUN
ap-296	39	8	an	an	DET
ap-296	39	9	flnn	flnn	NOUN
ap-296	39	10	consists	consist	VERB
ap-296	39	11	of	of	ADP
ap-296	39	12	several	several	ADJ
ap-296	39	13	layers	layer	NOUN
ap-296	39	14	[	[	X
ap-296	39	15	7	7	NUM
ap-296	39	16	]	]	PUNCT
ap-296	39	17	,	,	PUNCT
ap-296	39	18	[	[	X
ap-296	39	19	9	9	NUM
ap-296	39	20	]	]	SYM
ap-296	39	21	:	:	PUNCT
ap-296	39	22	layer	layer	NOUN
ap-296	39	23	1	1	NUM
ap-296	39	24	:	:	PUNCT
ap-296	39	25	actual	actual	ADJ
ap-296	39	26	values	value	NOUN
ap-296	39	27	of	of	ADP
ap-296	39	28	the	the	DET
ap-296	39	29	input	input	NOUN
ap-296	39	30	variables	variable	NOUN
ap-296	39	31	are	be	AUX
ap-296	39	32	stored	store	VERB
ap-296	39	33	in	in	ADP
ap-296	39	34	this	this	DET
ap-296	39	35	layer	layer	NOUN
ap-296	39	36	.	.	PUNCT
ap-296	40	1	generally	generally	ADV
ap-296	40	2	,	,	PUNCT
ap-296	40	3	fuzzy	fuzzy	ADJ
ap-296	40	4	sets	set	NOUN
ap-296	40	5	are	be	AUX
ap-296	40	6	considered	consider	VERB
ap-296	40	7	as	as	ADP
ap-296	40	8	the	the	DET
ap-296	40	9	input	input	NOUN
ap-296	40	10	values	value	NOUN
ap-296	40	11	(	(	PUNCT
ap-296	40	12	crisp	crisp	ADJ
ap-296	40	13	numbers	number	NOUN
ap-296	40	14	are	be	AUX
ap-296	40	15	special	special	ADJ
ap-296	40	16	cases	case	NOUN
ap-296	40	17	of	of	ADP
ap-296	40	18	fuzzy	fuzzy	ADJ
ap-296	40	19	sets	set	NOUN
ap-296	40	20	)	)	PUNCT
ap-296	40	21	.	.	PUNCT
ap-296	41	1	the	the	DET
ap-296	41	2	fuzzy	fuzzy	ADJ
ap-296	41	3	sets	set	NOUN
ap-296	41	4	are	be	AUX
ap-296	41	5	in	in	ADP
ap-296	41	6	parametric	parametric	ADJ
ap-296	41	7	form	form	NOUN
ap-296	41	8	or	or	CCONJ
ap-296	41	9	in	in	ADP
ap-296	41	10	look	look	VERB
ap-296	41	11	-	-	PUNCT
ap-296	41	12	up	up	ADP
ap-296	41	13	table	table	NOUN
ap-296	41	14	form	form	NOUN
ap-296	41	15	.	.	PUNCT
ap-296	42	1	layer	layer	NOUN
ap-296	42	2	2	2	NUM
ap-296	42	3	:	:	PUNCT
ap-296	42	4	rule	rule	NOUN
ap-296	42	5	premises	premise	NOUN
ap-296	42	6	(	(	PUNCT
ap-296	42	7	input	input	NOUN
ap-296	42	8	reference	reference	NOUN
ap-296	42	9	fuzzy	fuzzy	ADJ
ap-296	42	10	sets	set	NOUN
ap-296	42	11	)	)	PUNCT
ap-296	42	12	are	be	AUX
ap-296	42	13	stored	store	VERB
ap-296	42	14	here	here	ADV
ap-296	42	15	.	.	PUNCT
ap-296	43	1	the	the	DET
ap-296	43	2	actual	actual	ADJ
ap-296	43	3	input	input	NOUN
ap-296	43	4	value	value	NOUN
ap-296	43	5	is	be	AUX
ap-296	43	6	compared	compare	VERB
ap-296	43	7	with	with	ADP
ap-296	43	8	the	the	DET
ap-296	43	9	rule	rule	NOUN
ap-296	43	10	premise	premise	NOUN
ap-296	43	11	using	use	VERB
ap-296	43	12	degree	degree	NOUN
ap-296	43	13	of	of	ADP
ap-296	43	14	overlapping	overlap	VERB
ap-296	43	15	:	:	PUNCT
ap-296	43	16	�	�	PROPN
ap-296	43	17	�	�	PROPN
ap-296	43	18	�	�	PROPN
ap-296	43	19	�	�	PROPN
ap-296	43	20	�	�	PROPN
ap-296	43	21	�	�	PROPN
ap-296	43	22	�	�	PROPN
ap-296	43	23	�	�	PROPN
ap-296	43	24	�	�	PROPN
ap-296	43	25	�	�	PROPN
ap-296	43	26	d	d	PROPN
ap-296	43	27	x	x	X
ap-296	43	28	a	a	DET
ap-296	43	29	t	t	NOUN
ap-296	43	30	x	x	PUNCT
ap-296	43	31	x	x	X
ap-296	43	32	a	a	PRON
ap-296	43	33	x	x	X
ap-296	43	34	at	at	ADP
ap-296	43	35	x	x	PROPN
ap-296	43	36	t	t	PROPN
ap-296	43	37	,	,	PUNCT
ap-296	43	38	sup	sup	NOUN
ap-296	43	39	,	,	PUNCT
ap-296	43	40	�	�	PROPN
ap-296	43	41	�	�	PROPN
ap-296	43	42	�	�	PROPN
ap-296	43	43	hgt	hgt	PROPN
ap-296	43	44	x	x	X
ap-296	43	45	(	(	PUNCT
ap-296	43	46	1	1	NUM
ap-296	43	47	)	)	PUNCT
ap-296	43	48	where	where	SCONJ
ap-296	43	49	t	t	PROPN
ap-296	43	50	is	be	AUX
ap-296	43	51	the	the	DET
ap-296	43	52	selected	select	VERB
ap-296	43	53	t	t	NOUN
ap-296	43	54	-	-	PUNCT
ap-296	43	55	norm	norm	NOUN
ap-296	43	56	and	and	CCONJ
ap-296	43	57	hgt	hgt	PROPN
ap-296	43	58	is	be	AUX
ap-296	43	59	the	the	DET
ap-296	43	60	height	height	NOUN
ap-296	43	61	of	of	ADP
ap-296	43	62	intersection	intersection	NOUN
ap-296	43	63	of	of	ADP
ap-296	43	64	x	x	PUNCT
ap-296	43	65	and	and	CCONJ
ap-296	43	66	a	a	DET
ap-296	43	67	with	with	ADP
ap-296	43	68	respect	respect	NOUN
ap-296	43	69	to	to	ADP
ap-296	43	70	t	t	NOUN
ap-296	43	71	-	-	PUNCT
ap-296	43	72	norm	norm	NOUN
ap-296	43	73	t.	t.	NOUN
ap-296	43	74	in	in	ADP
ap-296	43	75	the	the	DET
ap-296	43	76	special	special	ADJ
ap-296	43	77	case	case	NOUN
ap-296	43	78	of	of	ADP
ap-296	43	79	crisp	crisp	ADJ
ap-296	43	80	input	input	NOUN
ap-296	43	81	x	x	PUNCT
ap-296	43	82	�	�	NOUN
ap-296	43	83	x	x	SYM
ap-296	43	84	*	*	PUNCT
ap-296	43	85	the	the	DET
ap-296	43	86	dt	dt	X
ap-296	43	87	(	(	PUNCT
ap-296	43	88	x	x	NOUN
ap-296	43	89	,	,	PUNCT
ap-296	43	90	a	a	PRON
ap-296	43	91	)	)	PUNCT
ap-296	43	92	is	be	AUX
ap-296	43	93	simply	simply	ADV
ap-296	43	94	�	�	PROPN
ap-296	43	95	�	�	PROPN
ap-296	43	96	�	�	PROPN
ap-296	43	97	�	�	PROPN
ap-296	43	98	d	d	PROPN
ap-296	43	99	x	x	X
ap-296	43	100	a	a	DET
ap-296	43	101	a	a	DET
ap-296	43	102	xt	xt	PROPN
ap-296	43	103	,	,	PUNCT
ap-296	43	104	*	*	PROPN
ap-296	43	105	�	�	PROPN
ap-296	43	106	(	(	PUNCT
ap-296	43	107	2	2	NUM
ap-296	43	108	)	)	PUNCT
ap-296	43	109	for	for	ADP
ap-296	43	110	some	some	DET
ap-296	43	111	parametric	parametric	ADJ
ap-296	43	112	fuzzy	fuzzy	ADJ
ap-296	43	113	sets	set	NOUN
ap-296	43	114	and	and	CCONJ
ap-296	43	115	some	some	DET
ap-296	43	116	t	t	NOUN
ap-296	43	117	-	-	PUNCT
ap-296	43	118	norms	norm	NOUN
ap-296	43	119	for	for	ADP
ap-296	43	120	dt	dt	PROPN
ap-296	43	121	(	(	PUNCT
ap-296	43	122	x	x	NOUN
ap-296	43	123	,	,	PUNCT
ap-296	43	124	a	a	PRON
ap-296	43	125	)	)	PUNCT
ap-296	43	126	an	an	DET
ap-296	43	127	analytical	analytical	ADJ
ap-296	43	128	expression	expression	NOUN
ap-296	43	129	can	can	AUX
ap-296	43	130	be	be	AUX
ap-296	43	131	derived	derive	VERB
ap-296	43	132	.	.	PUNCT
ap-296	44	1	for	for	ADP
ap-296	44	2	the	the	DET
ap-296	44	3	other	other	ADJ
ap-296	44	4	cases	case	NOUN
ap-296	44	5	it	it	PRON
ap-296	44	6	must	must	AUX
ap-296	44	7	be	be	AUX
ap-296	44	8	computed	compute	VERB
ap-296	44	9	numerically	numerically	ADV
ap-296	44	10	.	.	PUNCT
ap-296	45	1	layer	layer	NOUN
ap-296	45	2	3	3	NUM
ap-296	45	3	:	:	PUNCT
ap-296	45	4	every	every	DET
ap-296	45	5	neuron	neuron	NOUN
ap-296	45	6	in	in	ADP
ap-296	45	7	this	this	DET
ap-296	45	8	layer	layer	NOUN
ap-296	45	9	performs	perform	VERB
ap-296	45	10	a	a	DET
ap-296	45	11	fuzzy	fuzzy	ADJ
ap-296	45	12	conjunction	conjunction	NOUN
ap-296	45	13	using	use	VERB
ap-296	45	14	the	the	DET
ap-296	45	15	selected	select	VERB
ap-296	45	16	t	t	NOUN
ap-296	45	17	-	-	PUNCT
ap-296	45	18	norm	norm	NOUN
ap-296	45	19	.	.	PUNCT
ap-296	46	1	�	�	PROPN
ap-296	46	2	�	�	PROPN
ap-296	46	3	y	y	PROPN
ap-296	46	4	t	t	PROPN
ap-296	46	5	u	u	X
ap-296	46	6	u	u	PROPN
ap-296	46	7	u	u	NOUN
ap-296	46	8	�	�	PROPN
ap-296	46	9	1	1	NUM
ap-296	46	10	2	2	NUM
ap-296	46	11	,	,	PUNCT
ap-296	46	12	,	,	PUNCT
ap-296	46	13	,	,	PUNCT
ap-296	46	14	�	�	PROPN
ap-296	46	15	n	n	CCONJ
ap-296	46	16	(	(	PUNCT
ap-296	46	17	3	3	X
ap-296	46	18	)	)	PUNCT
ap-296	46	19	the	the	DET
ap-296	46	20	common	common	ADJ
ap-296	46	21	parameter	parameter	NOUN
ap-296	46	22	of	of	ADP
ap-296	46	23	the	the	DET
ap-296	46	24	layer	layer	NOUN
ap-296	46	25	is	be	AUX
ap-296	46	26	the	the	DET
ap-296	46	27	type	type	NOUN
ap-296	46	28	of	of	ADP
ap-296	46	29	t	t	NOUN
ap-296	46	30	-	-	PUNCT
ap-296	46	31	norm	norm	NOUN
ap-296	46	32	and	and	CCONJ
ap-296	46	33	its	its	PRON
ap-296	46	34	parameters	parameter	NOUN
ap-296	46	35	.	.	PUNCT
ap-296	47	1	layer	layer	NOUN
ap-296	47	2	4	4	NUM
ap-296	47	3	:	:	PUNCT
ap-296	47	4	every	every	DET
ap-296	47	5	neuron	neuron	NOUN
ap-296	47	6	represents	represent	VERB
ap-296	47	7	a	a	DET
ap-296	47	8	rule	rule	NOUN
ap-296	47	9	weight	weight	NOUN
ap-296	47	10	w.	w.	NOUN
ap-296	47	11	the	the	DET
ap-296	47	12	output	output	NOUN
ap-296	47	13	of	of	ADP
ap-296	47	14	the	the	DET
ap-296	47	15	neuron	neuron	NOUN
ap-296	47	16	is	be	AUX
ap-296	47	17	the	the	DET
ap-296	47	18	overall	overall	ADJ
ap-296	47	19	degree	degree	NOUN
ap-296	47	20	of	of	ADP
ap-296	47	21	rule	rule	NOUN
ap-296	47	22	activation	activation	NOUN
ap-296	47	23	act	act	NOUN
ap-296	47	24	,	,	PUNCT
ap-296	47	25	and	and	CCONJ
ap-296	47	26	is	be	AUX
ap-296	47	27	computed	compute	VERB
ap-296	47	28	as	as	SCONJ
ap-296	47	29	follows	follow	VERB
ap-296	47	30	:	:	PUNCT
ap-296	47	31	y	y	PROPN
ap-296	47	32	act	act	PROPN
ap-296	47	33	w	w	PROPN
ap-296	47	34	u	u	PROPN
ap-296	47	35	�	�	PROPN
ap-296	47	36	�	�	PROPN
ap-296	47	37	�	�	PROPN
ap-296	47	38	(	(	PUNCT
ap-296	47	39	4	4	NUM
ap-296	47	40	)	)	PUNCT
ap-296	47	41	where	where	SCONJ
ap-296	47	42	parameter	parameter	PROPN
ap-296	47	43	w	w	PROPN
ap-296	47	44	has	have	VERB
ap-296	47	45	to	to	PART
ap-296	47	46	lie	lie	VERB
ap-296	47	47	in	in	ADP
ap-296	47	48	the	the	DET
ap-296	47	49	interval	interval	NOUN
ap-296	47	50	[	[	X
ap-296	47	51	0,1	0,1	NUM
ap-296	47	52	]	]	PUNCT
ap-296	47	53	.	.	PUNCT
ap-296	48	1	layer	layer	NOUN
ap-296	48	2	5	5	NUM
ap-296	48	3	:	:	PUNCT
ap-296	48	4	only	only	ADV
ap-296	48	5	rule	rule	NOUN
ap-296	48	6	consequents	consequent	NOUN
ap-296	48	7	(	(	PUNCT
ap-296	48	8	output	output	NOUN
ap-296	48	9	reference	reference	NOUN
ap-296	48	10	fuzzy	fuzzy	ADJ
ap-296	48	11	sets	set	NOUN
ap-296	48	12	)	)	PUNCT
ap-296	48	13	are	be	AUX
ap-296	48	14	stored	store	VERB
ap-296	48	15	in	in	ADP
ap-296	48	16	this	this	DET
ap-296	48	17	layer	layer	NOUN
ap-296	48	18	.	.	PUNCT
ap-296	49	1	the	the	DET
ap-296	49	2	fuzzy	fuzzy	ADJ
ap-296	49	3	sets	set	NOUN
ap-296	49	4	are	be	AUX
ap-296	49	5	usually	usually	ADV
ap-296	49	6	in	in	ADP
ap-296	49	7	the	the	DET
ap-296	49	8	parametric	parametric	ADJ
ap-296	49	9	form	form	NOUN
ap-296	49	10	.	.	PUNCT
ap-296	50	1	the	the	DET
ap-296	50	2	input	input	NOUN
ap-296	50	3	of	of	ADP
ap-296	50	4	the	the	DET
ap-296	50	5	neuron	neuron	NOUN
ap-296	50	6	is	be	AUX
ap-296	50	7	the	the	DET
ap-296	50	8	overall	overall	ADJ
ap-296	50	9	degree	degree	NOUN
ap-296	50	10	of	of	ADP
ap-296	50	11	rule	rule	NOUN
ap-296	50	12	activation	activation	NOUN
ap-296	50	13	act	act	PROPN
ap-296	50	14	.	.	PUNCT
ap-296	51	1	this	this	DET
ap-296	51	2	value	value	NOUN
ap-296	51	3	is	be	AUX
ap-296	51	4	attached	attach	VERB
ap-296	51	5	to	to	ADP
ap-296	51	6	the	the	DET
ap-296	51	7	reference	reference	NOUN
ap-296	51	8	fuzzy	fuzzy	ADJ
ap-296	51	9	set	set	NOUN
ap-296	51	10	,	,	PUNCT
ap-296	51	11	and	and	CCONJ
ap-296	51	12	together	together	ADV
ap-296	51	13	they	they	PRON
ap-296	51	14	are	be	AUX
ap-296	51	15	fed	feed	VERB
ap-296	51	16	to	to	ADP
ap-296	51	17	the	the	DET
ap-296	51	18	next	next	ADJ
ap-296	51	19	aggregation	aggregation	NOUN
ap-296	51	20	layer	layer	NOUN
ap-296	51	21	.	.	PUNCT
ap-296	52	1	layer	layer	NOUN
ap-296	52	2	6	6	NUM
ap-296	52	3	:	:	PUNCT
ap-296	52	4	the	the	DET
ap-296	52	5	output	output	NOUN
ap-296	52	6	of	of	ADP
ap-296	52	7	the	the	DET
ap-296	52	8	network	network	NOUN
ap-296	52	9	is	be	AUX
ap-296	52	10	computed	compute	VERB
ap-296	52	11	here	here	ADV
ap-296	52	12	,	,	PUNCT
ap-296	52	13	using	use	VERB
ap-296	52	14	the	the	DET
ap-296	52	15	selected	select	VERB
ap-296	52	16	aggregation	aggregation	NOUN
ap-296	52	17	(	(	PUNCT
ap-296	52	18	inference	inference	NOUN
ap-296	52	19	)	)	PUNCT
ap-296	52	20	algorithm	algorithm	NOUN
ap-296	52	21	.	.	PUNCT
ap-296	53	1	there	there	PRON
ap-296	53	2	is	be	VERB
ap-296	53	3	a	a	DET
ap-296	53	4	corresponding	correspond	VERB
ap-296	53	5	fuzzy	fuzzy	ADJ
ap-296	53	6	set	set	VERB
ap-296	53	7	bi	bi	NOUN
ap-296	53	8	with	with	ADP
ap-296	53	9	its	its	PRON
ap-296	53	10	activation	activation	NOUN
ap-296	53	11	degree	degree	NOUN
ap-296	53	12	acti	acti	NOUN
ap-296	53	13	in	in	ADP
ap-296	53	14	the	the	DET
ap-296	53	15	i	i	PROPN
ap-296	53	16	-	-	PUNCT
ap-296	53	17	th	th	X
ap-296	53	18	input	input	NOUN
ap-296	53	19	of	of	ADP
ap-296	53	20	each	each	DET
ap-296	53	21	neuron	neuron	NOUN
ap-296	53	22	.	.	PUNCT
ap-296	54	1	when	when	SCONJ
ap-296	54	2	we	we	PRON
ap-296	54	3	use	use	VERB
ap-296	54	4	the	the	DET
ap-296	54	5	mamdani	mamdani	PROPN
ap-296	54	6	inference	inference	NOUN
ap-296	54	7	algorithm	algorithm	NOUN
ap-296	54	8	,	,	PUNCT
ap-296	54	9	the	the	DET
ap-296	54	10	output	output	NOUN
ap-296	54	11	fuzzy	fuzzy	ADJ
ap-296	54	12	set	set	NOUN
ap-296	54	13	is	be	AUX
ap-296	54	14	computed	compute	VERB
ap-296	54	15	as	as	SCONJ
ap-296	54	16	follows	follow	VERB
ap-296	54	17	:	:	PUNCT
ap-296	54	18	�	�	PROPN
ap-296	54	19	�	�	PROPN
ap-296	54	20	�	�	PROPN
ap-296	54	21	�	�	PROPN
ap-296	54	22	�	�	PROPN
ap-296	54	23	�	�	PROPN
ap-296	54	24	y	y	PROPN
ap-296	54	25	y	y	PROPN
ap-296	54	26	t	t	PROPN
ap-296	54	27	act	act	PROPN
ap-296	54	28	b	b	PROPN
ap-296	54	29	y	y	PROPN
ap-296	54	30	i	i	PROPN
ap-296	54	31	n	n	PROPN
ap-296	54	32	�	�	PROPN
ap-296	54	33	�	�	PROPN
ap-296	54	34	max	max	PROPN
ap-296	54	35	,	,	PUNCT
ap-296	55	1	1	1	NUM
ap-296	55	2	i	i	PRON
ap-296	55	3	i	i	PRON
ap-296	55	4	(	(	PUNCT
ap-296	55	5	5	5	NUM
ap-296	55	6	)	)	PUNCT
ap-296	55	7	where	where	SCONJ
ap-296	55	8	n	n	PRON
ap-296	55	9	is	be	AUX
ap-296	55	10	the	the	DET
ap-296	55	11	number	number	NOUN
ap-296	55	12	of	of	ADP
ap-296	55	13	inputs	input	NOUN
ap-296	55	14	to	to	ADP
ap-296	55	15	the	the	DET
ap-296	55	16	neuron	neuron	NOUN
ap-296	55	17	.	.	PUNCT
ap-296	56	1	when	when	SCONJ
ap-296	56	2	we	we	PRON
ap-296	56	3	use	use	VERB
ap-296	56	4	a	a	DET
ap-296	56	5	fuzzy	fuzzy	ADJ
ap-296	56	6	arithmetic	arithmetic	ADJ
ap-296	56	7	based	base	VERB
ap-296	56	8	inference	inference	NOUN
ap-296	56	9	algorithm	algorithm	NOUN
ap-296	56	10	,	,	PUNCT
ap-296	56	11	the	the	DET
ap-296	56	12	output	output	NOUN
ap-296	56	13	fuzzy	fuzzy	ADJ
ap-296	56	14	set	set	NOUN
ap-296	56	15	is	be	AUX
ap-296	56	16	computed	compute	VERB
ap-296	56	17	as	as	SCONJ
ap-296	56	18	follows	follow	VERB
ap-296	56	19	:	:	PUNCT
ap-296	56	20	y	y	PROPN
ap-296	56	21	act	act	PROPN
ap-296	56	22	b	b	NUM
ap-296	56	23	act	act	NOUN
ap-296	56	24	i	i	PRON
ap-296	56	25	n	n	VERB
ap-296	57	1	i	i	PRON
ap-296	57	2	n	n	VERB
ap-296	58	1	i	i	PRON
ap-296	58	2	i	i	PRON
ap-296	59	1	i	i	PRON
ap-296	59	2	i	i	PROPN
ap-296	59	3	�	�	PROPN
ap-296	59	4	�	�	PROPN
ap-296	59	5	�	�	PROPN
ap-296	59	6	�	�	PROPN
ap-296	59	7	�	�	PROPN
ap-296	59	8	�	�	PROPN
ap-296	59	9	1	1	NUM
ap-296	59	10	1	1	NUM
ap-296	59	11	(	(	PUNCT
ap-296	59	12	6	6	NUM
ap-296	59	13	)	)	PUNCT
ap-296	59	14	usually	usually	ADV
ap-296	59	15	only	only	ADV
ap-296	59	16	crisp	crisp	ADJ
ap-296	59	17	output	output	NOUN
ap-296	59	18	value	value	NOUN
ap-296	59	19	y	y	PROPN
ap-296	59	20	is	be	AUX
ap-296	59	21	needed	need	VERB
ap-296	59	22	.	.	PUNCT
ap-296	60	1	then	then	ADV
ap-296	60	2	we	we	PRON
ap-296	60	3	use	use	VERB
ap-296	60	4	a	a	DET
ap-296	60	5	defuzzification	defuzzification	NOUN
ap-296	60	6	method	method	NOUN
ap-296	60	7	to	to	PART
ap-296	60	8	get	get	VERB
ap-296	60	9	the	the	DET
ap-296	60	10	crisp	crisp	ADJ
ap-296	60	11	value	value	NOUN
ap-296	60	12	.	.	PUNCT
ap-296	61	1	the	the	DET
ap-296	61	2	most	most	ADV
ap-296	61	3	widely	widely	ADV
ap-296	61	4	used	use	VERB
ap-296	61	5	method	method	NOUN
ap-296	61	6	is	be	AUX
ap-296	61	7	centroid	centroid	NOUN
ap-296	61	8	average	average	ADJ
ap-296	61	9	defuzzification	defuzzification	NOUN
ap-296	61	10	:	:	PUNCT
ap-296	61	11	y	y	PROPN
ap-296	61	12	act	act	VERB
ap-296	61	13	y	y	PROPN
ap-296	61	14	act	act	VERB
ap-296	61	15	i	i	PRON
ap-296	62	1	n	n	VERB
ap-296	62	2	i	i	PRON
ap-296	62	3	n	n	CCONJ
ap-296	62	4	�	�	PROPN
ap-296	62	5	�	�	PROPN
ap-296	62	6	�	�	PROPN
ap-296	62	7	�	�	PROPN
ap-296	62	8	�	�	PROPN
ap-296	62	9	�	�	PROPN
ap-296	62	10	i	i	PRON
ap-296	63	1	i	i	PRON
ap-296	63	2	i	i	VERB
ap-296	63	3	1	1	NUM
ap-296	63	4	1	1	NUM
ap-296	63	5	(	(	PUNCT
ap-296	63	6	6	6	NUM
ap-296	63	7	)	)	PUNCT
ap-296	63	8	where	where	SCONJ
ap-296	63	9	yi	yi	PROPN
ap-296	63	10	is	be	AUX
ap-296	63	11	a	a	DET
ap-296	63	12	centroid	centroid	NOUN
ap-296	63	13	of	of	ADP
ap-296	63	14	fuzzy	fuzzy	ADJ
ap-296	63	15	set	set	ADJ
ap-296	63	16	bi	bi	PROPN
ap-296	63	17	.	.	PROPN
ap-296	63	18	flnn	flnn	PROPN
ap-296	63	19	works	work	VERB
ap-296	63	20	in	in	ADP
ap-296	63	21	the	the	DET
ap-296	63	22	following	follow	VERB
ap-296	63	23	manner	manner	NOUN
ap-296	63	24	[	[	X
ap-296	63	25	7	7	NUM
ap-296	63	26	]	]	PUNCT
ap-296	63	27	,	,	PUNCT
ap-296	63	28	[	[	X
ap-296	63	29	9	9	NUM
ap-296	63	30	]	]	PUNCT
ap-296	63	31	.	.	PUNCT
ap-296	64	1	in	in	ADP
ap-296	64	2	the	the	DET
ap-296	64	3	forward	forward	ADJ
ap-296	64	4	run	run	NOUN
ap-296	64	5	the	the	DET
ap-296	64	6	input	input	NOUN
ap-296	64	7	values	value	NOUN
ap-296	64	8	(	(	PUNCT
ap-296	64	9	crisp	crisp	ADJ
ap-296	64	10	values	value	NOUN
ap-296	64	11	,	,	PUNCT
ap-296	64	12	fuzzy	fuzzy	ADJ
ap-296	64	13	sets	set	NOUN
ap-296	64	14	)	)	PUNCT
ap-296	64	15	are	be	AUX
ap-296	64	16	first	first	ADV
ap-296	64	17	compared	compare	VERB
ap-296	64	18	with	with	ADP
ap-296	64	19	all	all	DET
ap-296	64	20	premises	premise	NOUN
ap-296	64	21	of	of	ADP
ap-296	64	22	the	the	DET
ap-296	64	23	rules	rule	NOUN
ap-296	64	24	(	(	PUNCT
ap-296	64	25	input	input	NOUN
ap-296	64	26	reference	reference	NOUN
ap-296	64	27	fuzzy	fuzzy	ADJ
ap-296	64	28	sets	set	NOUN
ap-296	64	29	)	)	PUNCT
ap-296	64	30	.	.	PUNCT
ap-296	65	1	the	the	DET
ap-296	65	2	outputs	output	NOUN
ap-296	65	3	of	of	ADP
ap-296	65	4	the	the	DET
ap-296	65	5	and	and	CCONJ
ap-296	65	6	-	-	PUNCT
ap-296	65	7	neuron	neuron	NOUN
ap-296	65	8	are	be	AUX
ap-296	65	9	then	then	ADV
ap-296	65	10	combined	combine	VERB
ap-296	65	11	with	with	ADP
ap-296	65	12	rule	rule	NOUN
ap-296	65	13	-	-	PUNCT
ap-296	65	14	weight	weight	NOUN
ap-296	65	15	(	(	PUNCT
ap-296	65	16	preference	preference	NOUN
ap-296	65	17	between	between	ADP
ap-296	65	18	rules	rule	NOUN
ap-296	65	19	)	)	PUNCT
ap-296	65	20	to	to	PART
ap-296	65	21	obtain	obtain	VERB
ap-296	65	22	the	the	DET
ap-296	65	23	degree	degree	NOUN
ap-296	65	24	of	of	ADP
ap-296	65	25	rule	rule	NOUN
ap-296	65	26	activation	activation	NOUN
ap-296	65	27	.	.	PUNCT
ap-296	66	1	in	in	ADP
ap-296	66	2	the	the	DET
ap-296	66	3	last	last	ADJ
ap-296	66	4	layer	layer	NOUN
ap-296	66	5	these	these	DET
ap-296	66	6	degrees	degree	NOUN
ap-296	66	7	are	be	AUX
ap-296	66	8	aggregated	aggregate	VERB
ap-296	66	9	with	with	ADP
ap-296	66	10	the	the	DET
ap-296	66	11	corresponding	corresponding	ADJ
ap-296	66	12	consequents	consequent	NOUN
ap-296	66	13	of	of	ADP
ap-296	66	14	the	the	DET
ap-296	66	15	rules	rule	NOUN
ap-296	66	16	(	(	PUNCT
ap-296	66	17	output	output	NOUN
ap-296	66	18	reference	reference	NOUN
ap-296	66	19	fuzzy	fuzzy	ADJ
ap-296	66	20	sets	set	NOUN
ap-296	66	21	)	)	PUNCT
ap-296	66	22	according	accord	VERB
ap-296	66	23	to	to	ADP
ap-296	66	24	the	the	DET
ap-296	66	25	inference	inference	NOUN
ap-296	66	26	algorithm	algorithm	NOUN
ap-296	66	27	.	.	PUNCT
ap-296	67	1	the	the	DET
ap-296	67	2	output	output	NOUN
ap-296	67	3	of	of	ADP
ap-296	67	4	the	the	DET
ap-296	67	5	flnn	flnn	NOUN
ap-296	67	6	can	can	AUX
ap-296	67	7	be	be	AUX
ap-296	67	8	a	a	DET
ap-296	67	9	fuzzy	fuzzy	ADJ
ap-296	67	10	set	set	NOUN
ap-296	67	11	or	or	CCONJ
ap-296	67	12	a	a	DET
ap-296	67	13	crisp	crisp	ADJ
ap-296	67	14	value	value	NOUN
ap-296	67	15	(	(	PUNCT
ap-296	67	16	after	after	ADP
ap-296	67	17	defuzzification	defuzzification	NOUN
ap-296	67	18	)	)	PUNCT
ap-296	67	19	.	.	PUNCT
ap-296	68	1	3	3	NUM
ap-296	68	2	determining	determine	VERB
ap-296	68	3	constraints	constraint	NOUN
ap-296	68	4	of	of	ADP
ap-296	68	5	mf	mf	NOUN
ap-296	68	6	parameters	parameter	NOUN
ap-296	68	7	in	in	ADP
ap-296	68	8	the	the	DET
ap-296	68	9	case	case	NOUN
ap-296	68	10	of	of	ADP
ap-296	68	11	flnn	flnn	NOUN
ap-296	68	12	the	the	DET
ap-296	68	13	membership	membership	NOUN
ap-296	68	14	functions	function	VERB
ap-296	68	15	mfj	mfj	PROPN
ap-296	68	16	(	(	PUNCT
ap-296	68	17	xi	xi	PROPN
ap-296	68	18	)	)	PUNCT
ap-296	68	19	of	of	ADP
ap-296	68	20	input	input	NOUN
ap-296	68	21	xi	xi	X
ap-296	68	22	and	and	CCONJ
ap-296	68	23	output	output	PROPN
ap-296	68	24	y	y	PROPN
ap-296	68	25	are	be	AUX
ap-296	68	26	frequently	frequently	ADV
ap-296	68	27	approximated	approximate	VERB
ap-296	68	28	by	by	ADP
ap-296	68	29	gaussians	gaussian	NOUN
ap-296	68	30	.	.	PUNCT
ap-296	69	1	a	a	DET
ap-296	69	2	gaussian	gaussian	ADJ
ap-296	69	3	shape	shape	NOUN
ap-296	69	4	is	be	AUX
ap-296	69	5	formed	form	VERB
ap-296	69	6	by	by	ADP
ap-296	69	7	two	two	NUM
ap-296	69	8	parameters	parameter	NOUN
ap-296	69	9	:	:	PUNCT
ap-296	69	10	mathematical	mathematical	ADJ
ap-296	69	11	expectation	expectation	NOUN
ap-296	69	12	c	c	NOUN
ap-296	69	13	and	and	CCONJ
ap-296	69	14	standard	standard	ADJ
ap-296	69	15	deviation	deviation	NOUN
ap-296	69	16	�	�	PROPN
ap-296	69	17	as	as	ADP
ap-296	69	18	in	in	ADP
ap-296	69	19	formula	formula	NOUN
ap-296	69	20	(	(	PUNCT
ap-296	69	21	8)	8)	NUM
ap-296	69	22	:	:	PUNCT
ap-296	69	23	�	�	PROPN
ap-296	69	24	�	�	PROPN
ap-296	69	25	�	�	PROPN
ap-296	69	26	�	�	PROPN
ap-296	69	27	�	�	PROPN
ap-296	69	28	�	�	PROPN
ap-296	69	29	mf	mf	VERB
ap-296	69	30	x	x	PUNCT
ap-296	69	31	g	g	NOUN
ap-296	69	32	x	x	X
ap-296	69	33	c	c	NOUN
ap-296	69	34	e	e	X
ap-296	69	35	x	x	X
ap-296	69	36	c	c	VERB
ap-296	69	37	j	j	PROPN
ap-296	70	1	i	i	PRON
ap-296	70	2	j	j	VERB
ap-296	71	1	i	i	PRON
ap-296	71	2	j	j	PROPN
ap-296	72	1	j	j	INTJ
ap-296	73	1	i	i	PRON
ap-296	73	2	j	j	PROPN
ap-296	74	1	j	j	PROPN
ap-296	74	2	2	2	NUM
ap-296	74	3	�	�	PROPN
ap-296	74	4	�	�	PROPN
ap-296	74	5	�	�	PROPN
ap-296	74	6	�	�	PROPN
ap-296	74	7	�	�	PROPN
ap-296	74	8	�	�	PROPN
ap-296	74	9	�	�	PROPN
ap-296	74	10	�	�	PROPN
ap-296	74	11	�	�	PROPN
ap-296	74	12	�	�	PROPN
ap-296	74	13	�	�	PROPN
ap-296	74	14	,	,	PUNCT
ap-296	74	15	,	,	PUNCT
ap-296	74	16	�	�	PROPN
ap-296	74	17	�	�	PROPN
ap-296	74	18	2	2	NUM
ap-296	74	19	2	2	NUM
ap-296	74	20	(	(	PUNCT
ap-296	74	21	8)	8)	NUM
ap-296	74	22	the	the	DET
ap-296	74	23	idea	idea	NOUN
ap-296	74	24	of	of	ADP
ap-296	74	25	2nd	2nd	ADJ
ap-296	74	26	order	order	NOUN
ap-296	74	27	fuzzy	fuzzy	ADJ
ap-296	74	28	set	set	NOUN
ap-296	74	29	was	be	AUX
ap-296	74	30	introduced	introduce	VERB
ap-296	74	31	to	to	PART
ap-296	74	32	get	get	VERB
ap-296	74	33	the	the	DET
ap-296	74	34	boundary	boundary	NOUN
ap-296	74	35	of	of	ADP
ap-296	74	36	gaussian	gaussian	ADJ
ap-296	74	37	shape	shape	NOUN
ap-296	74	38	of	of	ADP
ap-296	74	39	membership	membership	NOUN
ap-296	74	40	function	function	NOUN
ap-296	74	41	by	by	ADP
ap-296	74	42	[	[	X
ap-296	74	43	11	11	NUM
ap-296	74	44	]	]	PUNCT
ap-296	74	45	.	.	PUNCT
ap-296	75	1	the	the	DET
ap-296	75	2	2nd	2nd	ADJ
ap-296	75	3	order	order	NOUN
ap-296	75	4	fuzzy	fuzzy	ADJ
ap-296	75	5	set	set	NOUN
ap-296	75	6	of	of	ADP
ap-296	75	7	a	a	DET
ap-296	75	8	given	give	VERB
ap-296	75	9	mf	mf	X
ap-296	75	10	(	(	PUNCT
ap-296	75	11	x	x	X
ap-296	75	12	)	)	PUNCT
ap-296	75	13	is	be	AUX
ap-296	75	14	the	the	DET
ap-296	75	15	area	area	NOUN
ap-296	75	16	between	between	ADP
ap-296	75	17	d+	d+	PROPN
ap-296	75	18	and	and	CCONJ
ap-296	75	19	d	d	PROPN
ap-296	75	20	�	�	PROPN
ap-296	75	21	,	,	PUNCT
ap-296	75	22	where	where	SCONJ
ap-296	75	23	d+	d+	X
ap-296	75	24	,	,	PUNCT
ap-296	75	25	and	and	CCONJ
ap-296	75	26	d	d	X
ap-296	75	27	�	�	PROPN
ap-296	75	28	are	be	AUX
ap-296	75	29	the	the	DET
ap-296	75	30	upper	upper	ADJ
ap-296	75	31	and	and	CCONJ
ap-296	75	32	the	the	DET
ap-296	75	33	lower	low	ADJ
ap-296	75	34	crisp	crisp	ADJ
ap-296	75	35	boundaries	boundary	NOUN
ap-296	75	36	of	of	ADP
ap-296	75	37	the	the	DET
ap-296	75	38	2nd	2nd	ADJ
ap-296	75	39	order	order	NOUN
ap-296	75	40	fuzzy	fuzzy	ADJ
ap-296	75	41	sets	set	NOUN
ap-296	75	42	,	,	PUNCT
ap-296	75	43	respectively	respectively	ADV
ap-296	75	44	,	,	PUNCT
ap-296	75	45	as	as	SCONJ
ap-296	75	46	shown	show	VERB
ap-296	75	47	in	in	ADP
ap-296	75	48	figure	figure	NOUN
ap-296	75	49	2	2	NUM
ap-296	76	1	[	[	X
ap-296	76	2	2	2	NUM
ap-296	76	3	]	]	PUNCT
ap-296	76	4	.	.	PUNCT
ap-296	77	1	the	the	DET
ap-296	77	2	expressions	expression	NOUN
ap-296	77	3	to	to	PART
ap-296	77	4	determine	determine	VERB
ap-296	77	5	its	its	PRON
ap-296	77	6	crisp	crisp	ADJ
ap-296	77	7	boundaries	boundary	NOUN
ap-296	77	8	are	be	AUX
ap-296	77	9	(	(	PUNCT
ap-296	77	10	9	9	NUM
ap-296	77	11	)	)	PUNCT
ap-296	77	12	,	,	PUNCT
ap-296	77	13	and	and	CCONJ
ap-296	77	14	(	(	PUNCT
ap-296	77	15	10	10	NUM
ap-296	77	16	):	):	PUNCT
ap-296	77	17	�	�	PROPN
ap-296	77	18	�	�	PROPN
ap-296	77	19	�	�	PROPN
ap-296	77	20	�	�	PROPN
ap-296	77	21	�	�	PROPN
ap-296	77	22	�	�	PROPN
ap-296	77	23	d	d	PROPN
ap-296	77	24	x	x	PROPN
ap-296	77	25	mf	mf	VERB
ap-296	77	26	xj	xj	PROPN
ap-296	78	1	i	i	PRON
ap-296	78	2	j	j	PROPN
ap-296	79	1	i	i	PRON
ap-296	79	2	�	�	PROPN
ap-296	79	3	�	�	PROPN
ap-296	79	4	�	�	PROPN
ap-296	79	5	min	min	PROPN
ap-296	79	6	,	,	PUNCT
ap-296	79	7	1	1	NUM
ap-296	79	8	�	�	PROPN
ap-296	79	9	(	(	PUNCT
ap-296	79	10	9	9	NUM
ap-296	79	11	)	)	PUNCT
ap-296	79	12	�	�	PROPN
ap-296	79	13	�	�	PROPN
ap-296	79	14	�	�	PROPN
ap-296	79	15	�	�	PROPN
ap-296	79	16	�	�	PROPN
ap-296	79	17	�	�	PROPN
ap-296	79	18	d	d	PROPN
ap-296	79	19	x	x	PROPN
ap-296	79	20	mf	mf	VERB
ap-296	79	21	xj	xj	PROPN
ap-296	80	1	i	i	PRON
ap-296	80	2	j	j	PROPN
ap-296	81	1	i	i	PRON
ap-296	81	2	�	�	PROPN
ap-296	81	3	�	�	PROPN
ap-296	81	4	�	�	PROPN
ap-296	81	5	max	max	PROPN
ap-296	81	6	,	,	PUNCT
ap-296	81	7	0	0	NUM
ap-296	81	8	�	�	PROPN
ap-296	81	9	(	(	PUNCT
ap-296	81	10	10	10	NUM
ap-296	81	11	)	)	PUNCT
ap-296	81	12	formula	formula	NOUN
ap-296	81	13	(	(	PUNCT
ap-296	81	14	9	9	NUM
ap-296	81	15	)	)	PUNCT
ap-296	81	16	and	and	CCONJ
ap-296	81	17	formula	formula	NOUN
ap-296	81	18	(	(	PUNCT
ap-296	81	19	10	10	NUM
ap-296	81	20	)	)	PUNCT
ap-296	81	21	are	be	AUX
ap-296	81	22	based	base	VERB
ap-296	81	23	on	on	ADP
ap-296	81	24	the	the	DET
ap-296	81	25	assumptions	assumption	NOUN
ap-296	81	26	that	that	SCONJ
ap-296	81	27	the	the	DET
ap-296	81	28	height	height	NOUN
ap-296	81	29	of	of	ADP
ap-296	81	30	the	the	DET
ap-296	81	31	slice	slice	NOUN
ap-296	81	32	of	of	ADP
ap-296	81	33	the	the	DET
ap-296	81	34	2nd	2nd	ADJ
ap-296	81	35	order	order	NOUN
ap-296	81	36	fuzzy	fuzzy	ADJ
ap-296	81	37	region	region	NOUN
ap-296	81	38	,	,	PUNCT
ap-296	81	39	bounded	bound	VERB
ap-296	81	40	by	by	ADP
ap-296	81	41	d+	d+	NOUN
ap-296	81	42	and	and	CCONJ
ap-296	81	43	d	d	PROPN
ap-296	81	44	�	�	PROPN
ap-296	81	45	,	,	PUNCT
ap-296	81	46	at	at	ADP
ap-296	81	47	point	point	NOUN
ap-296	81	48	x	x	VERB
ap-296	81	49	is	be	AUX
ap-296	81	50	equal	equal	ADJ
ap-296	81	51	to	to	ADP
ap-296	81	52	2	2	NUM
ap-296	81	53	�	�	PROPN
ap-296	81	54	where	where	SCONJ
ap-296	81	55	�	�	PROPN
ap-296	81	56	�	�	PROPN
ap-296	82	1	[	[	X
ap-296	82	2	0	0	NUM
ap-296	82	3	,	,	PUNCT
ap-296	82	4	0.3679	0.3679	NUM
ap-296	82	5	]	]	PUNCT
ap-296	82	6	and	and	CCONJ
ap-296	82	7	these	these	DET
ap-296	82	8	boundaries	boundary	NOUN
ap-296	82	9	are	be	AUX
ap-296	82	10	equidistant	equidistant	ADJ
ap-296	82	11	from	from	ADP
ap-296	82	12	mf	mf	PRON
ap-296	82	13	(	(	PUNCT
ap-296	82	14	x	x	NOUN
ap-296	82	15	)	)	PUNCT
ap-296	82	16	.	.	PUNCT
ap-296	83	1	to	to	PART
ap-296	83	2	obtain	obtain	VERB
ap-296	83	3	the	the	DET
ap-296	83	4	ranges	range	NOUN
ap-296	83	5	for	for	ADP
ap-296	83	6	the	the	DET
ap-296	83	7	shape	shape	NOUN
ap-296	83	8	forming	form	VERB
ap-296	83	9	parameters	parameter	NOUN
ap-296	83	10	of	of	ADP
ap-296	83	11	the	the	DET
ap-296	83	12	mfs	mfs	PROPN
ap-296	83	13	,	,	PUNCT
ap-296	83	14	it	it	PRON
ap-296	83	15	should	should	AUX
ap-296	83	16	be	be	AUX
ap-296	83	17	assumed	assume	VERB
ap-296	83	18	that	that	SCONJ
ap-296	83	19	these	these	DET
ap-296	83	20	2nd	2nd	ADJ
ap-296	83	21	order	order	NOUN
ap-296	83	22	fuzzy	fuzzy	ADJ
ap-296	83	23	sets	set	NOUN
ap-296	83	24	are	be	AUX
ap-296	83	25	mf	mf	NOUN
ap-296	83	26	search	search	NOUN
ap-296	83	27	spaces	space	NOUN
ap-296	83	28	.	.	PUNCT
ap-296	84	1	therefore	therefore	ADV
ap-296	84	2	,	,	PUNCT
ap-296	84	3	all	all	PRON
ap-296	84	4	mfs	mfs	VERB
ap-296	84	5	with	with	ADP
ap-296	84	6	acceptable	acceptable	ADJ
ap-296	84	7	parameters	parameter	NOUN
ap-296	84	8	should	should	AUX
ap-296	84	9	be	be	AUX
ap-296	84	10	inside	inside	ADP
ap-296	84	11	the	the	DET
ap-296	84	12	area	area	NOUN
ap-296	84	13	.	.	PUNCT
ap-296	85	1	in	in	ADP
ap-296	85	2	the	the	DET
ap-296	85	3	general	general	ADJ
ap-296	85	4	case	case	NOUN
ap-296	85	5	the	the	DET
ap-296	85	6	intervals	interval	NOUN
ap-296	85	7	of	of	ADP
ap-296	85	8	acceptable	acceptable	ADJ
ap-296	85	9	values	value	NOUN
ap-296	85	10	for	for	ADP
ap-296	85	11	every	every	DET
ap-296	85	12	mf	mf	NOUN
ap-296	85	13	shape	shape	NOUN
ap-296	85	14	forming	form	VERB
ap-296	85	15	parameter	parameter	NOUN
ap-296	85	16	(	(	PUNCT
ap-296	85	17	e.g.	e.g.	ADV
ap-296	85	18	,	,	PUNCT
ap-296	85	19	�	�	PROPN
ap-296	85	20	c	c	PROPN
ap-296	85	21	�	�	PROPN
ap-296	86	1	[	[	X
ap-296	86	2	c11	c11	NOUN
ap-296	86	3	,	,	PUNCT
ap-296	86	4	c22	c22	NOUN
ap-296	86	5	]	]	PUNCT
ap-296	86	6	,	,	PUNCT
ap-296	86	7	and	and	CCONJ
ap-296	86	8	�	�	PROPN
ap-296	86	9	�	�	PROPN
ap-296	86	10	�	�	PROPN
ap-296	86	11	[	[	X
ap-296	86	12	�	�	PROPN
ap-296	86	13	11	11	NUM
ap-296	86	14	,	,	PUNCT
ap-296	86	15	�	�	PROPN
ap-296	86	16	22	22	NUM
ap-296	86	17	]	]	PUNCT
ap-296	86	18	for	for	ADP
ap-296	86	19	gaussians	gaussian	NOUN
ap-296	86	20	)	)	PUNCT
ap-296	86	21	may	may	AUX
ap-296	86	22	be	be	AUX
ap-296	86	23	determined	determine	VERB
ap-296	86	24	by	by	ADP
ap-296	86	25	solving	solve	VERB
ap-296	86	26	formulas	formula	NOUN
ap-296	86	27	(	(	PUNCT
ap-296	86	28	8)	8)	NUM
ap-296	86	29	,	,	PUNCT
ap-296	86	30	(	(	PUNCT
ap-296	86	31	9	9	NUM
ap-296	86	32	)	)	PUNCT
ap-296	86	33	,	,	PUNCT
ap-296	86	34	and	and	CCONJ
ap-296	86	35	(	(	PUNCT
ap-296	86	36	10	10	NUM
ap-296	86	37	)	)	PUNCT
ap-296	86	38	.	.	PUNCT
ap-296	87	1	in	in	ADP
ap-296	87	2	practice	practice	NOUN
ap-296	87	3	,	,	PUNCT
ap-296	87	4	this	this	PRON
ap-296	87	5	may	may	AUX
ap-296	87	6	be	be	AUX
ap-296	87	7	done	do	VERB
ap-296	87	8	approximately	approximately	ADV
ap-296	87	9	considering	consider	VERB
ap-296	87	10	d+	d+	NOUN
ap-296	87	11	and	and	CCONJ
ap-296	87	12	d	d	X
ap-296	87	13	�	�	PROPN
ap-296	87	14	as	as	ADP
ap-296	87	15	soft	soft	ADJ
ap-296	87	16	constraints	constraint	NOUN
ap-296	87	17	.	.	PUNCT
ap-296	88	1	for	for	ADP
ap-296	88	2	instance	instance	NOUN
ap-296	88	3	,	,	PUNCT
ap-296	88	4	c11	c11	NOUN
ap-296	88	5	and	and	CCONJ
ap-296	88	6	c22	c22	NOUN
ap-296	88	7	for	for	ADP
ap-296	88	8	gaussians	gaussian	NOUN
ap-296	88	9	may	may	AUX
ap-296	88	10	be	be	AUX
ap-296	88	11	found	find	VERB
ap-296	88	12	as	as	ADP
ap-296	88	13	the	the	DET
ap-296	88	14	maximum	maximum	ADJ
ap-296	88	15	root	root	NOUN
ap-296	88	16	and	and	CCONJ
ap-296	88	17	the	the	DET
ap-296	88	18	minimum	minimum	ADJ
ap-296	88	19	root	root	NOUN
ap-296	88	20	of	of	ADP
ap-296	88	21	the	the	DET
ap-296	88	22	equation	equation	NOUN
ap-296	88	23	d+	d+	PUNCT
ap-296	88	24	�	�	PROPN
ap-296	88	25	1	1	NUM
ap-296	88	26	,	,	PUNCT
ap-296	88	27	which	which	PRON
ap-296	88	28	can	can	AUX
ap-296	88	29	easily	easily	ADV
ap-296	88	30	is	be	AUX
ap-296	88	31	to	to	PART
ap-296	88	32	be	be	AUX
ap-296	88	33	calculated	calculate	VERB
ap-296	88	34	.	.	PUNCT
ap-296	89	1	this	this	DET
ap-296	89	2	equation	equation	NOUN
ap-296	89	3	is	be	AUX
ap-296	89	4	based	base	VERB
ap-296	89	5	on	on	ADP
ap-296	89	6	the	the	DET
ap-296	89	7	assumption	assumption	NOUN
ap-296	89	8	that	that	SCONJ
ap-296	89	9	a	a	DET
ap-296	89	10	fuzzy	fuzzy	ADJ
ap-296	89	11	set	set	NOUN
ap-296	89	12	represented	represent	VERB
ap-296	89	13	by	by	ADP
ap-296	89	14	the	the	DET
ap-296	89	15	gaussian	gaussian	NOUN
ap-296	89	16	must	must	AUX
ap-296	89	17	have	have	AUX
ap-296	89	18	a	a	DET
ap-296	89	19	point	point	NOUN
ap-296	89	20	where	where	SCONJ
ap-296	89	21	it	it	PRON
ap-296	89	22	is	be	AUX
ap-296	89	23	absolutely	absolutely	ADV
ap-296	89	24	true	true	ADJ
ap-296	89	25	.	.	PUNCT
ap-296	90	1	�	�	PROPN
ap-296	90	2	11	11	NUM
ap-296	90	3	,	,	PUNCT
ap-296	90	4	and	and	CCONJ
ap-296	90	5	�	�	PROPN
ap-296	90	6	22	22	NUM
ap-296	90	7	can	can	AUX
ap-296	90	8	easily	easily	ADV
ap-296	90	9	be	be	AUX
ap-296	90	10	found	find	VERB
ap-296	90	11	from	from	ADP
ap-296	90	12	the	the	DET
ap-296	90	13	following	follow	VERB
ap-296	90	14	four	four	NUM
ap-296	90	15	equations	equation	NOUN
ap-296	90	16	:	:	PUNCT
ap-296	90	17	�	�	PROPN
ap-296	90	18	�	�	PROPN
ap-296	90	19	�	�	PROPN
ap-296	90	20	�	�	PROPN
ap-296	90	21	�	�	PROPN
ap-296	90	22	�	�	PROPN
ap-296	90	23	�	�	PROPN
ap-296	90	24	�	�	PROPN
ap-296	91	1	g	g	PROPN
ap-296	91	2	c	c	PROPN
ap-296	91	3	c	c	NOUN
ap-296	91	4	g	g	PROPN
ap-296	91	5	c	c	NOUN
ap-296	91	6	c	c	NOUN
ap-296	91	7	g	g	PROPN
ap-296	91	8	c	c	NOUN
ap-296	91	9	c	c	NOUN
ap-296	91	10	g	g	PROPN
ap-296	91	11	c	c	PROPN
ap-296	91	12	c	c	PROPN
ap-296	91	13	�	�	PROPN
ap-296	91	14	�	�	PROPN
ap-296	91	15	�	�	PROPN
ap-296	91	16	�	�	PROPN
ap-296	91	17	�	�	PROPN
ap-296	91	18	�	�	PROPN
ap-296	91	19	�	�	PROPN
ap-296	91	20	�	�	PROPN
ap-296	91	21	�	�	PROPN
ap-296	91	22	�	�	PROPN
ap-296	91	23	�	�	PROPN
ap-296	91	24	�	�	PROPN
ap-296	91	25	�	�	PROPN
ap-296	91	26	�	�	PROPN
ap-296	91	27	�	�	PROPN
ap-296	91	28	�	�	PROPN
ap-296	91	29	�	�	PROPN
ap-296	91	30	�	�	PROPN
ap-296	91	31	,	,	PUNCT
ap-296	91	32	,	,	PUNCT
ap-296	91	33	,	,	PUNCT
ap-296	91	34	,	,	PUNCT
ap-296	91	35	;	;	PUNCT
ap-296	91	36	,	,	PUNCT
ap-296	91	37	,	,	PUNCT
ap-296	91	38	,	,	PUNCT
ap-296	91	39	,	,	PUNCT
ap-296	91	40	22	22	NUM
ap-296	91	41	11	11	NUM
ap-296	91	42	and	and	CCONJ
ap-296	91	43	(	(	PUNCT
ap-296	91	44	11	11	NUM
ap-296	91	45	)	)	PUNCT
ap-296	91	46	�	�	PROPN
ap-296	91	47	�	�	PROPN
ap-296	91	48	�	�	PROPN
ap-296	91	49	�	�	PROPN
ap-296	91	50	�	�	PROPN
ap-296	91	51	�	�	PROPN
ap-296	91	52	�	�	PROPN
ap-296	91	53	�	�	PROPN
ap-296	92	1	g	g	PROPN
ap-296	92	2	c	c	PROPN
ap-296	92	3	c	c	NOUN
ap-296	92	4	g	g	PROPN
ap-296	92	5	c	c	NOUN
ap-296	92	6	c	c	NOUN
ap-296	92	7	g	g	PROPN
ap-296	92	8	c	c	NOUN
ap-296	92	9	c	c	NOUN
ap-296	92	10	g	g	PROPN
ap-296	92	11	c	c	PROPN
ap-296	92	12	c	c	PROPN
ap-296	92	13	�	�	PROPN
ap-296	92	14	�	�	PROPN
ap-296	92	15	�	�	PROPN
ap-296	92	16	�	�	PROPN
ap-296	92	17	�	�	PROPN
ap-296	92	18	�	�	PROPN
ap-296	92	19	�	�	PROPN
ap-296	92	20	�	�	PROPN
ap-296	92	21	�	�	PROPN
ap-296	92	22	�	�	PROPN
ap-296	92	23	�	�	PROPN
ap-296	92	24	�	�	PROPN
ap-296	92	25	�	�	PROPN
ap-296	92	26	�	�	PROPN
ap-296	92	27	�	�	PROPN
ap-296	92	28	�	�	PROPN
ap-296	92	29	�	�	PROPN
ap-296	92	30	�	�	PROPN
ap-296	92	31	,	,	PUNCT
ap-296	92	32	,	,	PUNCT
ap-296	92	33	,	,	PUNCT
ap-296	92	34	,	,	PUNCT
ap-296	92	35	;	;	PUNCT
ap-296	92	36	,	,	PUNCT
ap-296	92	37	,	,	PUNCT
ap-296	92	38	,	,	PUNCT
ap-296	92	39	,	,	PUNCT
ap-296	92	40	22	22	NUM
ap-296	92	41	11	11	NUM
ap-296	92	42	and	and	CCONJ
ap-296	92	43	(	(	PUNCT
ap-296	92	44	12	12	NUM
ap-296	92	45	)	)	PUNCT
ap-296	92	46	where	where	SCONJ
ap-296	92	47	we	we	PRON
ap-296	92	48	choose	choose	VERB
ap-296	92	49	�	�	NOUN
ap-296	92	50	11	11	NUM
ap-296	92	51	as	as	ADP
ap-296	92	52	minimum	minimum	NOUN
ap-296	92	53	and	and	CCONJ
ap-296	92	54	�	�	NOUN
ap-296	92	55	22	22	NUM
ap-296	92	56	as	as	ADV
ap-296	92	57	maximum	maximum	ADJ
ap-296	92	58	from	from	ADP
ap-296	92	59	the	the	DET
ap-296	92	60	roots	root	NOUN
ap-296	92	61	.	.	PUNCT
ap-296	93	1	these	these	DET
ap-296	93	2	equations	equation	NOUN
ap-296	93	3	are	be	AUX
ap-296	93	4	based	base	VERB
ap-296	93	5	on	on	ADP
ap-296	93	6	the	the	DET
ap-296	93	7	assumption	assumption	NOUN
ap-296	93	8	that	that	SCONJ
ap-296	93	9	the	the	DET
ap-296	93	10	acceptable	acceptable	ADJ
ap-296	93	11	gaussians	gaussian	NOUN
ap-296	93	12	with	with	ADP
ap-296	93	13	[	[	X
ap-296	93	14	�	�	NOUN
ap-296	93	15	11	11	NUM
ap-296	93	16	,	,	PUNCT
ap-296	93	17	�	�	PROPN
ap-296	93	18	22	22	NUM
ap-296	93	19	]	]	PUNCT
ap-296	93	20	should	should	AUX
ap-296	93	21	cross	cross	VERB
ap-296	93	22	the	the	DET
ap-296	93	23	2nd	2nd	ADJ
ap-296	93	24	order	order	NOUN
ap-296	93	25	fuzzy	fuzzy	ADJ
ap-296	93	26	region	region	NOUN
ap-296	93	27	slices	slice	NOUN
ap-296	93	28	at	at	ADP
ap-296	93	29	points	point	NOUN
ap-296	93	30	x	x	PUNCT
ap-296	93	31	�	�	PROPN
ap-296	93	32	c	c	PROPN
ap-296	93	33	�	�	PROPN
ap-296	93	34	�	�	PROPN
ap-296	93	35	.	.	PUNCT
ap-296	94	1	there	there	PRON
ap-296	94	2	are	be	VERB
ap-296	94	3	two	two	NUM
ap-296	94	4	options	option	NOUN
ap-296	94	5	finding	find	VERB
ap-296	94	6	the	the	DET
ap-296	94	7	constraints	constraint	NOUN
ap-296	94	8	of	of	ADP
ap-296	94	9	gaussian	gaussian	ADJ
ap-296	94	10	parameters	parameter	NOUN
ap-296	94	11	.	.	PUNCT
ap-296	95	1	70	70	NUM
ap-296	96	1	©	©	PROPN
ap-296	96	2	czech	czech	PROPN
ap-296	96	3	technical	technical	PROPN
ap-296	96	4	university	university	PROPN
ap-296	96	5	publishing	publishing	NOUN
ap-296	96	6	house	house	NOUN
ap-296	96	7	http://ctn.cvut.cz/ap/	http://ctn.cvut.cz/ap/	PROPN
ap-296	96	8	acta	acta	PROPN
ap-296	96	9	polytechnica	polytechnica	PROPN
ap-296	96	10	vol	vol	NOUN
ap-296	96	11	.	.	PUNCT
ap-296	97	1	41	41	NUM
ap-296	97	2	no	no	NOUN
ap-296	97	3	.	.	PUNCT
ap-296	98	1	6/2001	6/2001	NUM
ap-296	98	2	first	first	ADV
ap-296	98	3	,	,	PUNCT
ap-296	98	4	considering	consider	VERB
ap-296	98	5	constraints	constraint	NOUN
ap-296	98	6	as	as	ADP
ap-296	98	7	a	a	DET
ap-296	98	8	hard	hard	ADJ
ap-296	98	9	constraint	constraint	NOUN
ap-296	98	10	and	and	CCONJ
ap-296	98	11	it	it	PRON
ap-296	98	12	follows	follow	VERB
ap-296	98	13	:	:	PUNCT
ap-296	98	14	the	the	DET
ap-296	98	15	lower	low	ADJ
ap-296	98	16	and	and	CCONJ
ap-296	98	17	upper	upper	ADJ
ap-296	98	18	bounds	bound	NOUN
ap-296	98	19	of	of	ADP
ap-296	98	20	the	the	DET
ap-296	98	21	center	center	NOUN
ap-296	98	22	of	of	ADP
ap-296	98	23	the	the	DET
ap-296	98	24	gaussian	gaussian	ADJ
ap-296	98	25	membership	membership	NOUN
ap-296	98	26	function	function	NOUN
ap-296	98	27	will	will	AUX
ap-296	98	28	be	be	AUX
ap-296	98	29	chosen	choose	VERB
ap-296	98	30	as	as	ADP
ap-296	98	31	cmin	cmin	NOUN
ap-296	98	32	,	,	PUNCT
ap-296	98	33	and	and	CCONJ
ap-296	98	34	cmax	cmax	NOUN
ap-296	98	35	should	should	AUX
ap-296	98	36	be	be	AUX
ap-296	98	37	less	less	ADJ
ap-296	98	38	than	than	ADP
ap-296	98	39	the	the	DET
ap-296	98	40	values	value	NOUN
ap-296	98	41	of	of	ADP
ap-296	98	42	c11	c11	NOUN
ap-296	98	43	and	and	CCONJ
ap-296	98	44	c22	c22	NOUN
ap-296	98	45	to	to	PART
ap-296	98	46	satisfy	satisfy	VERB
ap-296	98	47	the	the	DET
ap-296	98	48	search	search	NOUN
ap-296	98	49	space	space	NOUN
ap-296	98	50	constraint	constraint	NOUN
ap-296	98	51	conditions	condition	NOUN
ap-296	98	52	of	of	ADP
ap-296	98	53	2nd	2nd	ADJ
ap-296	98	54	order	order	NOUN
ap-296	98	55	fuzzy	fuzzy	ADJ
ap-296	98	56	sets	set	NOUN
ap-296	98	57	as	as	SCONJ
ap-296	98	58	shown	show	VERB
ap-296	98	59	in	in	ADP
ap-296	98	60	fig	fig	NOUN
ap-296	98	61	.	.	PUNCT
ap-296	99	1	3	3	X
ap-296	99	2	.	.	X
ap-296	99	3	the	the	DET
ap-296	99	4	lower	low	ADJ
ap-296	99	5	,	,	PUNCT
ap-296	99	6	and	and	CCONJ
ap-296	99	7	upper	upper	ADJ
ap-296	99	8	bounds	bound	NOUN
ap-296	99	9	for	for	ADP
ap-296	99	10	spread	spread	NOUN
ap-296	99	11	of	of	ADP
ap-296	99	12	gaussian	gaussian	ADJ
ap-296	99	13	membership	membership	NOUN
ap-296	99	14	function	function	PROPN
ap-296	99	15	�	�	PROPN
ap-296	99	16	min	min	PROPN
ap-296	99	17	,	,	PUNCT
ap-296	99	18	and	and	CCONJ
ap-296	99	19	�	�	PROPN
ap-296	99	20	max	max	PROPN
ap-296	99	21	will	will	AUX
ap-296	99	22	be	be	AUX
ap-296	99	23	equal	equal	ADJ
ap-296	99	24	to	to	ADP
ap-296	99	25	�	�	PROPN
ap-296	99	26	11	11	NUM
ap-296	99	27	and	and	CCONJ
ap-296	99	28	�	�	PROPN
ap-296	99	29	22	22	NUM
ap-296	99	30	,	,	PUNCT
ap-296	99	31	respectively	respectively	ADV
ap-296	99	32	to	to	PART
ap-296	99	33	satisfy	satisfy	VERB
ap-296	99	34	search	search	NOUN
ap-296	99	35	space	space	NOUN
ap-296	99	36	constraint	constraint	NOUN
ap-296	99	37	conditions	condition	NOUN
ap-296	99	38	of	of	ADP
ap-296	99	39	2nd	2nd	ADJ
ap-296	99	40	order	order	NOUN
ap-296	99	41	fuzzy	fuzzy	ADJ
ap-296	99	42	sets	set	NOUN
ap-296	99	43	,	,	PUNCT
ap-296	99	44	as	as	SCONJ
ap-296	99	45	shown	show	VERB
ap-296	99	46	in	in	ADP
ap-296	99	47	fig	fig	NOUN
ap-296	99	48	.	.	PUNCT
ap-296	100	1	2	2	X
ap-296	100	2	.	.	X
ap-296	100	3	a	a	DET
ap-296	100	4	second	second	ADJ
ap-296	100	5	option	option	NOUN
ap-296	100	6	is	be	AUX
ap-296	100	7	to	to	PART
ap-296	100	8	consider	consider	VERB
ap-296	100	9	these	these	DET
ap-296	100	10	constraints	constraint	NOUN
ap-296	100	11	as	as	ADP
ap-296	100	12	a	a	DET
ap-296	100	13	soft	soft	ADJ
ap-296	100	14	constraint	constraint	NOUN
ap-296	100	15	,	,	PUNCT
ap-296	100	16	i.e.	i.e.	X
ap-296	100	17	,	,	PUNCT
ap-296	100	18	[	[	X
ap-296	100	19	cmin	cmin	NOUN
ap-296	100	20	,	,	PUNCT
ap-296	100	21	cmax	cmax	NOUN
ap-296	100	22	]	]	PUNCT
ap-296	100	23	equal	equal	ADJ
ap-296	100	24	[	[	PUNCT
ap-296	100	25	c11	c11	NOUN
ap-296	100	26	,	,	PUNCT
ap-296	100	27	c22	c22	NOUN
ap-296	100	28	]	]	PUNCT
ap-296	100	29	,	,	PUNCT
ap-296	100	30	and	and	CCONJ
ap-296	101	1	[	[	X
ap-296	101	2	�	�	PROPN
ap-296	101	3	min	min	PROPN
ap-296	101	4	,	,	PUNCT
ap-296	101	5	�	�	PROPN
ap-296	101	6	max	max	PROPN
ap-296	101	7	]	]	X
ap-296	101	8	equal	equal	ADJ
ap-296	101	9	[	[	X
ap-296	101	10	�	�	NOUN
ap-296	101	11	11	11	NUM
ap-296	101	12	,	,	PUNCT
ap-296	101	13	�	�	PROPN
ap-296	101	14	22	22	NUM
ap-296	101	15	]	]	PUNCT
ap-296	101	16	.	.	PUNCT
ap-296	102	1	4	4	NUM
ap-296	102	2	linear	linear	ADJ
ap-296	102	3	adapted	adapt	VERB
ap-296	102	4	genetic	genetic	ADJ
ap-296	102	5	algorithm	algorithm	NOUN
ap-296	102	6	(	(	PUNCT
ap-296	102	7	laga	laga	NOUN
ap-296	102	8	)	)	PUNCT
ap-296	102	9	for	for	ADP
ap-296	102	10	tuning	tune	VERB
ap-296	102	11	mf	mf	NOUN
ap-296	102	12	parameters	parameter	NOUN
ap-296	102	13	a	a	DET
ap-296	102	14	particular	particular	ADJ
ap-296	102	15	evolutionary	evolutionary	ADJ
ap-296	102	16	algorithm	algorithm	NOUN
ap-296	102	17	a	a	DET
ap-296	102	18	genetic	genetic	ADJ
ap-296	102	19	algorithm	algorithm	NOUN
ap-296	102	20	is	be	AUX
ap-296	102	21	chosen	choose	VERB
ap-296	102	22	.	.	PUNCT
ap-296	103	1	varying	vary	VERB
ap-296	103	2	the	the	DET
ap-296	103	3	crossover	crossover	NOUN
ap-296	103	4	probability	probability	NOUN
ap-296	103	5	rate	rate	NOUN
ap-296	103	6	pc	pc	NOUN
ap-296	103	7	and	and	CCONJ
ap-296	103	8	mutation	mutation	NOUN
ap-296	103	9	probability	probability	NOUN
ap-296	103	10	rate	rate	NOUN
ap-296	103	11	pm	pm	AUX
ap-296	103	12	the	the	DET
ap-296	103	13	ga	ga	PROPN
ap-296	103	14	’s	’s	PART
ap-296	103	15	control	control	NOUN
ap-296	103	16	parameters	parameter	NOUN
ap-296	103	17	provide	provide	VERB
ap-296	103	18	faster	fast	ADJ
ap-296	103	19	convergence	convergence	NOUN
ap-296	103	20	than	than	ADP
ap-296	103	21	constant	constant	ADJ
ap-296	103	22	probability	probability	NOUN
ap-296	103	23	rates	rate	NOUN
ap-296	103	24	.	.	PUNCT
ap-296	104	1	pc	pc	NOUN
ap-296	104	2	is	be	AUX
ap-296	104	3	set	set	VERB
ap-296	104	4	high	high	ADV
ap-296	104	5	at	at	ADP
ap-296	104	6	the	the	DET
ap-296	104	7	beginning	beginning	NOUN
ap-296	104	8	of	of	ADP
ap-296	104	9	the	the	DET
ap-296	104	10	generation	generation	NOUN
ap-296	104	11	and	and	CCONJ
ap-296	104	12	decreases	decrease	VERB
ap-296	104	13	linearly	linearly	ADV
ap-296	104	14	with	with	ADP
ap-296	104	15	generations	generation	NOUN
ap-296	104	16	as	as	ADP
ap-296	104	17	in	in	ADP
ap-296	104	18	(	(	PUNCT
ap-296	104	19	13	13	NUM
ap-296	104	20	)	)	PUNCT
ap-296	105	1	[	[	X
ap-296	105	2	1	1	NUM
ap-296	105	3	]	]	PUNCT
ap-296	105	4	.	.	PUNCT
ap-296	106	1	as	as	SCONJ
ap-296	106	2	is	be	AUX
ap-296	106	3	known	know	VERB
ap-296	106	4	from	from	ADP
ap-296	106	5	the	the	DET
ap-296	106	6	standard	standard	ADJ
ap-296	106	7	genetic	genetic	ADJ
ap-296	106	8	algorithm	algorithm	NOUN
ap-296	106	9	(	(	PUNCT
ap-296	106	10	sga	sga	PROPN
ap-296	106	11	)	)	PUNCT
ap-296	106	12	,	,	PUNCT
ap-296	106	13	at	at	ADP
ap-296	106	14	the	the	DET
ap-296	106	15	beginning	beginning	NOUN
ap-296	106	16	of	of	ADP
ap-296	106	17	generation	generation	NOUN
ap-296	106	18	the	the	DET
ap-296	106	19	randomized	randomized	ADJ
ap-296	106	20	initial	initial	ADJ
ap-296	106	21	ga	ga	NOUN
ap-296	106	22	population	population	NOUN
ap-296	106	23	is	be	AUX
ap-296	106	24	diverse	diverse	ADJ
ap-296	106	25	.	.	PUNCT
ap-296	107	1	this	this	PRON
ap-296	107	2	means	mean	VERB
ap-296	107	3	that	that	SCONJ
ap-296	107	4	promising	promise	VERB
ap-296	107	5	solutions	solution	NOUN
ap-296	107	6	are	be	AUX
ap-296	107	7	scattered	scatter	VERB
ap-296	107	8	through	through	ADP
ap-296	107	9	the	the	DET
ap-296	107	10	search	search	NOUN
ap-296	107	11	space	space	NOUN
ap-296	107	12	.	.	PUNCT
ap-296	108	1	so	so	ADV
ap-296	108	2	,	,	PUNCT
ap-296	108	3	pc	pc	NOUN
ap-296	108	4	is	be	AUX
ap-296	108	5	high	high	ADJ
ap-296	108	6	in	in	ADP
ap-296	108	7	the	the	DET
ap-296	108	8	initial	initial	ADJ
ap-296	108	9	generation	generation	NOUN
ap-296	108	10	,	,	PUNCT
ap-296	108	11	but	but	CCONJ
ap-296	108	12	over	over	ADP
ap-296	108	13	the	the	DET
ap-296	108	14	generations	generation	NOUN
ap-296	108	15	these	these	DET
ap-296	108	16	solutions	solution	NOUN
ap-296	108	17	will	will	AUX
ap-296	108	18	generate	generate	VERB
ap-296	108	19	even	even	ADV
ap-296	108	20	better	well	ADJ
ap-296	108	21	solutions	solution	NOUN
ap-296	108	22	.	.	PUNCT
ap-296	109	1	this	this	PRON
ap-296	109	2	means	mean	VERB
ap-296	109	3	that	that	SCONJ
ap-296	109	4	the	the	DET
ap-296	109	5	population	population	NOUN
ap-296	109	6	converges	converge	VERB
ap-296	109	7	to	to	ADP
ap-296	109	8	a	a	DET
ap-296	109	9	smaller	small	ADJ
ap-296	109	10	subset	subset	NOUN
ap-296	109	11	of	of	ADP
ap-296	109	12	the	the	DET
ap-296	109	13	search	search	NOUN
ap-296	109	14	space	space	NOUN
ap-296	109	15	,	,	PUNCT
ap-296	109	16	and	and	CCONJ
ap-296	109	17	the	the	DET
ap-296	109	18	pc	pc	NOUN
ap-296	109	19	value	value	NOUN
ap-296	109	20	will	will	AUX
ap-296	109	21	decrease	decrease	VERB
ap-296	109	22	according	accord	VERB
ap-296	109	23	to	to	ADP
ap-296	109	24	formula	formula	NOUN
ap-296	109	25	(	(	PUNCT
ap-296	109	26	13	13	NUM
ap-296	109	27	)	)	PUNCT
ap-296	109	28	,	,	PUNCT
ap-296	109	29	where	where	SCONJ
ap-296	109	30	there	there	PRON
ap-296	109	31	are	be	VERB
ap-296	109	32	large	large	ADJ
ap-296	109	33	values	value	NOUN
ap-296	109	34	for	for	ADP
ap-296	109	35	pc(0.5	pc(0.5	NOUN
ap-296	109	36	�	�	PROPN
ap-296	109	37	1.0	1.0	NUM
ap-296	109	38	)	)	PUNCT
ap-296	109	39	,	,	PUNCT
ap-296	109	40	and	and	CCONJ
ap-296	109	41	small	small	ADJ
ap-296	109	42	values	value	NOUN
ap-296	109	43	of	of	ADP
ap-296	109	44	pm(0.0	pm(0.0	NOUN
ap-296	109	45	�	�	NOUN
ap-296	109	46	0.005	0.005	NUM
ap-296	109	47	)	)	PUNCT
ap-296	110	1	[	[	X
ap-296	110	2	6	6	NUM
ap-296	110	3	]	]	PUNCT
ap-296	110	4	,	,	PUNCT
ap-296	110	5	[	[	X
ap-296	110	6	13	13	NUM
ap-296	110	7	]	]	PUNCT
ap-296	110	8	,	,	PUNCT
ap-296	110	9	and	and	CCONJ
ap-296	110	10	[	[	X
ap-296	110	11	3	3	NUM
ap-296	110	12	]	]	PUNCT
ap-296	110	13	.	.	PUNCT
ap-296	111	1	�	�	PROPN
ap-296	111	2	�	�	PROPN
ap-296	111	3	�	�	PROPN
ap-296	111	4	�	�	PROPN
ap-296	111	5	p	p	NOUN
ap-296	111	6	x	x	NOUN
ap-296	111	7	.	.	PUNCT
ap-296	112	1	x	x	X
ap-296	113	1	m	m	NOUN
ap-296	113	2	x	x	PUNCT
ap-296	113	3	x	x	X
ap-296	113	4	mc	mc	PROPN
ap-296	113	5	�	�	PROPN
ap-296	113	6	�	�	PROPN
ap-296	113	7	�	�	PROPN
ap-296	113	8	�	�	PROPN
ap-296	113	9	�	�	PROPN
ap-296	113	10	05	05	NUM
ap-296	113	11	1	1	NUM
ap-296	113	12	1	1	NUM
ap-296	113	13	1	1	NUM
ap-296	113	14	1	1	NUM
ap-296	113	15	(	(	PUNCT
ap-296	113	16	)	)	PUNCT
ap-296	113	17	,	,	PUNCT
ap-296	113	18	,	,	PUNCT
ap-296	113	19	,	,	PUNCT
ap-296	113	20	(	(	PUNCT
ap-296	113	21	13	13	NUM
ap-296	113	22	)	)	PUNCT
ap-296	113	23	where	where	SCONJ
ap-296	113	24	x	x	PRON
ap-296	113	25	is	be	AUX
ap-296	113	26	the	the	DET
ap-296	113	27	number	number	NOUN
ap-296	113	28	of	of	ADP
ap-296	113	29	generations	generation	NOUN
ap-296	113	30	,	,	PUNCT
ap-296	113	31	and	and	CCONJ
ap-296	113	32	m	m	PROPN
ap-296	113	33	is	be	AUX
ap-296	113	34	the	the	DET
ap-296	113	35	maximum	maximum	NOUN
ap-296	113	36	of	of	ADP
ap-296	113	37	generations	generation	NOUN
ap-296	113	38	allowed	allow	VERB
ap-296	113	39	.	.	PUNCT
ap-296	114	1	as	as	SCONJ
ap-296	114	2	is	be	AUX
ap-296	114	3	known	know	VERB
ap-296	114	4	,	,	PUNCT
ap-296	114	5	mutation	mutation	NOUN
ap-296	114	6	is	be	AUX
ap-296	114	7	not	not	PART
ap-296	114	8	needed	need	VERB
ap-296	114	9	at	at	ADP
ap-296	114	10	the	the	DET
ap-296	114	11	beginning	beginning	NOUN
ap-296	114	12	of	of	ADP
ap-296	114	13	generation	generation	NOUN
ap-296	114	14	,	,	PUNCT
ap-296	114	15	where	where	SCONJ
ap-296	114	16	the	the	DET
ap-296	114	17	members	member	NOUN
ap-296	114	18	of	of	ADP
ap-296	114	19	the	the	DET
ap-296	114	20	population	population	NOUN
ap-296	114	21	are	be	AUX
ap-296	114	22	very	very	ADV
ap-296	114	23	distinct	distinct	ADJ
ap-296	114	24	.	.	PUNCT
ap-296	115	1	the	the	DET
ap-296	115	2	value	value	NOUN
ap-296	115	3	of	of	ADP
ap-296	115	4	pm	pm	NOUN
ap-296	115	5	increases	increase	VERB
ap-296	115	6	linearly	linearly	ADV
ap-296	115	7	as	as	ADP
ap-296	115	8	a	a	DET
ap-296	115	9	function	function	NOUN
ap-296	115	10	of	of	ADP
ap-296	115	11	the	the	DET
ap-296	115	12	number	number	NOUN
ap-296	115	13	of	of	ADP
ap-296	115	14	generations	generation	NOUN
ap-296	115	15	to	to	PART
ap-296	115	16	exploit	exploit	VERB
ap-296	115	17	the	the	DET
ap-296	115	18	improved	improved	ADJ
ap-296	115	19	solution	solution	NOUN
ap-296	115	20	in	in	ADP
ap-296	115	21	the	the	DET
ap-296	115	22	established	establish	VERB
ap-296	115	23	region	region	NOUN
ap-296	115	24	of	of	ADP
ap-296	115	25	the	the	DET
ap-296	115	26	current	current	ADJ
ap-296	115	27	best	good	ADJ
ap-296	115	28	solution	solution	NOUN
ap-296	115	29	.	.	PUNCT
ap-296	116	1	this	this	PRON
ap-296	116	2	is	be	AUX
ap-296	116	3	clear	clear	ADJ
ap-296	116	4	from	from	ADP
ap-296	116	5	equation	equation	NOUN
ap-296	116	6	(	(	PUNCT
ap-296	116	7	14	14	NUM
ap-296	116	8	)	)	PUNCT
ap-296	116	9	.	.	PUNCT
ap-296	117	1	�	�	PROPN
ap-296	117	2	�	�	PROPN
ap-296	117	3	�	�	PROPN
ap-296	117	4	�	�	PROPN
ap-296	117	5	p	p	NOUN
ap-296	117	6	x	x	NOUN
ap-296	117	7	m	m	NOUN
ap-296	117	8	x	x	SYM
ap-296	117	9	x	x	SYM
ap-296	117	10	mm	mm	PROPN
ap-296	117	11	�	�	PROPN
ap-296	117	12	�	�	PROPN
ap-296	117	13	�	�	PROPN
ap-296	117	14	0005	0005	NUM
ap-296	117	15	1	1	NUM
ap-296	117	16	1	1	NUM
ap-296	117	17	1	1	NUM
ap-296	117	18	.	.	PUNCT
ap-296	118	1	(	(	PUNCT
ap-296	118	2	)	)	PUNCT
ap-296	118	3	,	,	PUNCT
ap-296	118	4	,	,	PUNCT
ap-296	118	5	.	.	PUNCT
ap-296	119	1	(	(	PUNCT
ap-296	119	2	14	14	NUM
ap-296	119	3	)	)	PUNCT
ap-296	119	4	5	5	NUM
ap-296	119	5	proposed	propose	VERB
ap-296	119	6	genetic	genetic	ADJ
ap-296	119	7	algorithm	algorithm	NOUN
ap-296	119	8	with	with	ADP
ap-296	119	9	constrained	constrain	VERB
ap-296	119	10	search	search	NOUN
ap-296	119	11	space	space	NOUN
ap-296	119	12	the	the	DET
ap-296	119	13	main	main	ADJ
ap-296	119	14	aspects	aspect	NOUN
ap-296	119	15	of	of	ADP
ap-296	119	16	the	the	DET
ap-296	119	17	proposed	propose	VERB
ap-296	119	18	laga	laga	NOUN
ap-296	119	19	for	for	ADP
ap-296	119	20	optimizing	optimize	VERB
ap-296	119	21	flnn	flnn	NOUN
ap-296	119	22	are	be	AUX
ap-296	119	23	discussed	discuss	VERB
ap-296	119	24	below	below	ADV
ap-296	119	25	and	and	CCONJ
ap-296	119	26	the	the	DET
ap-296	119	27	block	block	NOUN
ap-296	119	28	diagram	diagram	NOUN
ap-296	119	29	for	for	ADP
ap-296	119	30	the	the	DET
ap-296	119	31	laga	laga	NOUN
ap-296	119	32	optimization	optimization	NOUN
ap-296	119	33	process	process	NOUN
ap-296	119	34	is	be	AUX
ap-296	119	35	shown	show	VERB
ap-296	119	36	in	in	ADP
ap-296	119	37	fig	fig	NOUN
ap-296	119	38	.	.	PUNCT
ap-296	120	1	5	5	NUM
ap-296	120	2	.	.	SYM
ap-296	120	3	5.1	5.1	NUM
ap-296	120	4	fuzzy	fuzzy	ADJ
ap-296	120	5	model	model	NOUN
ap-296	120	6	representation	representation	NOUN
ap-296	120	7	this	this	DET
ap-296	120	8	section	section	NOUN
ap-296	120	9	discusses	discuss	VERB
ap-296	120	10	how	how	SCONJ
ap-296	120	11	the	the	DET
ap-296	120	12	proposed	propose	VERB
ap-296	120	13	flnn	flnn	NOUN
ap-296	120	14	is	be	AUX
ap-296	120	15	formulated	formulate	VERB
ap-296	120	16	using	use	VERB
ap-296	120	17	the	the	DET
ap-296	120	18	laga	laga	NOUN
ap-296	120	19	approach	approach	NOUN
ap-296	120	20	,	,	PUNCT
ap-296	120	21	where	where	SCONJ
ap-296	120	22	all	all	DET
ap-296	120	23	the	the	DET
ap-296	120	24	parameters	parameter	NOUN
ap-296	120	25	of	of	ADP
ap-296	120	26	the	the	DET
ap-296	120	27	flnn	flnn	NOUN
ap-296	120	28	are	be	AUX
ap-296	120	29	represented	represent	VERB
ap-296	120	30	in	in	ADP
ap-296	120	31	a	a	DET
ap-296	120	32	chromosome	chromosome	NOUN
ap-296	120	33	.	.	PUNCT
ap-296	121	1	the	the	DET
ap-296	121	2	chromosome	chromosome	NOUN
ap-296	121	3	representation	representation	NOUN
ap-296	121	4	determines	determine	VERB
ap-296	121	5	the	the	DET
ap-296	121	6	ga	ga	NOUN
ap-296	121	7	structure	structure	NOUN
ap-296	121	8	.	.	PUNCT
ap-296	122	1	with	with	ADP
ap-296	122	2	a	a	DET
ap-296	122	3	population	population	NOUN
ap-296	122	4	size	size	NOUN
ap-296	122	5	(	(	PUNCT
ap-296	122	6	popsize	popsize	PROPN
ap-296	122	7	)	)	PUNCT
ap-296	122	8	,	,	PUNCT
ap-296	122	9	we	we	PRON
ap-296	122	10	encode	encode	VERB
ap-296	122	11	the	the	DET
ap-296	122	12	parameters	parameter	NOUN
ap-296	122	13	of	of	ADP
ap-296	122	14	each	each	DET
ap-296	122	15	fuzzy	fuzzy	ADJ
ap-296	122	16	model	model	NOUN
ap-296	122	17	in	in	ADP
ap-296	122	18	a	a	DET
ap-296	122	19	chromosome	chromosome	NOUN
ap-296	122	20	,	,	PUNCT
ap-296	122	21	as	as	ADP
ap-296	122	22	a	a	DET
ap-296	122	23	sequence	sequence	NOUN
ap-296	122	24	of	of	ADP
ap-296	122	25	elements	element	NOUN
ap-296	122	26	describing	describe	VERB
ap-296	122	27	the	the	DET
ap-296	122	28	input	input	NOUN
ap-296	122	29	fuzzy	fuzzy	ADJ
ap-296	122	30	sets	set	NOUN
ap-296	122	31	in	in	ADP
ap-296	122	32	the	the	DET
ap-296	122	33	rule	rule	NOUN
ap-296	122	34	antecedents	antecedent	NOUN
ap-296	122	35	followed	follow	VERB
ap-296	122	36	by	by	ADP
ap-296	122	37	the	the	DET
ap-296	122	38	parameters	parameter	NOUN
ap-296	122	39	of	of	ADP
ap-296	122	40	weights	weight	NOUN
ap-296	122	41	and	and	CCONJ
ap-296	122	42	the	the	DET
ap-296	122	43	rule	rule	NOUN
ap-296	122	44	consequents	consequent	NOUN
ap-296	122	45	.	.	PUNCT
ap-296	123	1	where	where	SCONJ
ap-296	123	2	the	the	DET
ap-296	123	3	intervals	interval	NOUN
ap-296	123	4	of	of	ADP
ap-296	123	5	acceptable	acceptable	ADJ
ap-296	123	6	values	value	NOUN
ap-296	123	7	for	for	ADP
ap-296	123	8	every	every	DET
ap-296	123	9	mf	mf	NOUN
ap-296	123	10	shape	shape	NOUN
ap-296	123	11	forming	form	VERB
ap-296	123	12	parameter	parameter	NOUN
ap-296	123	13	(	(	PUNCT
ap-296	123	14	�	�	PROPN
ap-296	123	15	c	c	PROPN
ap-296	123	16	�	�	PROPN
ap-296	124	1	[	[	X
ap-296	124	2	cmin	cmin	NOUN
ap-296	124	3	,	,	PUNCT
ap-296	124	4	cmax	cmax	NOUN
ap-296	124	5	]	]	PUNCT
ap-296	124	6	,	,	PUNCT
ap-296	124	7	and	and	CCONJ
ap-296	124	8	�	�	PROPN
ap-296	124	9	�	�	PROPN
ap-296	124	10	�	�	PROPN
ap-296	124	11	[	[	X
ap-296	124	12	�	�	PROPN
ap-296	124	13	min	min	PROPN
ap-296	124	14	,	,	PUNCT
ap-296	124	15	�	�	PROPN
ap-296	124	16	max	max	PROPN
ap-296	124	17	]	]	PUNCT
ap-296	124	18	for	for	ADP
ap-296	124	19	gaussians	gaussian	NOUN
ap-296	124	20	)	)	PUNCT
ap-296	124	21	are	be	AUX
ap-296	124	22	determined	determine	VERB
ap-296	124	23	based	base	VERB
ap-296	124	24	on	on	ADP
ap-296	124	25	2nd	2nd	ADJ
ap-296	124	26	order	order	NOUN
ap-296	124	27	fuzzy	fuzzy	ADJ
ap-296	124	28	sets	set	NOUN
ap-296	124	29	for	for	ADP
ap-296	124	30	all	all	DET
ap-296	124	31	membership	membership	NOUN
ap-296	124	32	functions	function	NOUN
ap-296	124	33	,	,	PUNCT
ap-296	124	34	as	as	SCONJ
ap-296	124	35	explained	explain	VERB
ap-296	124	36	in	in	ADP
ap-296	124	37	section	section	NOUN
ap-296	124	38	4	4	NUM
ap-296	124	39	.	.	PUNCT
ap-296	125	1	the	the	DET
ap-296	125	2	acceptable	acceptable	ADJ
ap-296	125	3	constraints	constraint	NOUN
ap-296	125	4	for	for	ADP
ap-296	125	5	rule	rule	NOUN
ap-296	125	6	weights	weight	NOUN
ap-296	125	7	are	be	AUX
ap-296	125	8	between	between	ADP
ap-296	125	9	[	[	X
ap-296	125	10	0,1	0,1	NUM
ap-296	125	11	]	]	PUNCT
ap-296	125	12	,	,	PUNCT
ap-296	125	13	and	and	CCONJ
ap-296	125	14	for	for	ADP
ap-296	125	15	centroids	centroid	NOUN
ap-296	125	16	they	they	PRON
ap-296	125	17	are	be	AUX
ap-296	125	18	the	the	DET
ap-296	125	19	minimum	minimum	ADJ
ap-296	125	20	and	and	CCONJ
ap-296	125	21	maximum	maximum	ADJ
ap-296	125	22	values	value	NOUN
ap-296	125	23	of	of	ADP
ap-296	125	24	the	the	DET
ap-296	125	25	output	output	NOUN
ap-296	125	26	.	.	PUNCT
ap-296	126	1	5.2	5.2	NUM
ap-296	126	2	coding	coding	NOUN
ap-296	126	3	of	of	ADP
ap-296	126	4	flnn	flnn	NOUN
ap-296	126	5	parameters	parameter	NOUN
ap-296	126	6	fig	fig	PROPN
ap-296	126	7	.	.	PUNCT
ap-296	126	8	1	1	NUM
ap-296	126	9	shows	show	VERB
ap-296	126	10	n	n	NOUN
ap-296	126	11	inputs	input	NOUN
ap-296	126	12	(	(	PUNCT
ap-296	126	13	x1	x1	PROPN
ap-296	126	14	,	,	PUNCT
ap-296	126	15	x2	x2	PROPN
ap-296	126	16	,	,	PUNCT
ap-296	126	17	…	…	PUNCT
ap-296	126	18	,	,	PUNCT
ap-296	126	19	xn	xn	NUM
ap-296	126	20	)	)	PUNCT
ap-296	126	21	and	and	CCONJ
ap-296	126	22	one	one	NUM
ap-296	126	23	output	output	NOUN
ap-296	126	24	y.	y.	NOUN
ap-296	126	25	each	each	DET
ap-296	126	26	of	of	ADP
ap-296	126	27	the	the	DET
ap-296	126	28	input	input	NOUN
ap-296	126	29	fuzzy	fuzzy	ADJ
ap-296	126	30	variables	variable	NOUN
ap-296	126	31	is	be	AUX
ap-296	126	32	classified	classify	VERB
ap-296	126	33	into	into	ADP
ap-296	126	34	m	m	PROPN
ap-296	126	35	reference	reference	NOUN
ap-296	126	36	fuzzy	fuzzy	ADJ
ap-296	126	37	sets	set	NOUN
ap-296	126	38	.	.	PUNCT
ap-296	127	1	every	every	DET
ap-296	127	2	reference	reference	NOUN
ap-296	127	3	fuzzy	fuzzy	ADJ
ap-296	127	4	set	set	NOUN
ap-296	127	5	is	be	AUX
ap-296	127	6	described	describe	VERB
ap-296	127	7	by	by	ADP
ap-296	127	8	a	a	DET
ap-296	127	9	gaussian	gaussian	ADJ
ap-296	127	10	membership	membership	NOUN
ap-296	127	11	function	function	NOUN
ap-296	127	12	specified	specify	VERB
ap-296	127	13	by	by	ADP
ap-296	127	14	two	two	NUM
ap-296	127	15	parameters	parameter	NOUN
ap-296	127	16	:	:	PUNCT
ap-296	127	17	center	center	NOUN
ap-296	127	18	c	c	PROPN
ap-296	127	19	and	and	CCONJ
ap-296	127	20	spread	spread	VERB
ap-296	127	21	�	�	PROPN
ap-296	127	22	,	,	PUNCT
ap-296	127	23	resulting	result	VERB
ap-296	127	24	in	in	ADP
ap-296	127	25	(	(	PUNCT
ap-296	127	26	2	2	NUM
ap-296	127	27	×	×	NOUN
ap-296	127	28	m	m	NOUN
ap-296	127	29	×	×	NOUN
ap-296	127	30	n	n	CCONJ
ap-296	127	31	)	)	PUNCT
ap-296	127	32	parameters	parameter	NOUN
ap-296	127	33	at	at	ADP
ap-296	127	34	the	the	DET
ap-296	127	35	corresponding	corresponding	ADJ
ap-296	127	36	layer	layer	NOUN
ap-296	127	37	.	.	PUNCT
ap-296	128	1	using	use	VERB
ap-296	128	2	the	the	DET
ap-296	128	3	wang	wang	PROPN
ap-296	128	4	technique	technique	NOUN
ap-296	128	5	for	for	ADP
ap-296	128	6	generating	generate	VERB
ap-296	128	7	rules	rule	NOUN
ap-296	128	8	from	from	ADP
ap-296	128	9	given	give	VERB
ap-296	128	10	data	datum	NOUN
ap-296	128	11	[	[	X
ap-296	128	12	16	16	NUM
ap-296	128	13	]	]	PUNCT
ap-296	128	14	,	,	PUNCT
ap-296	128	15	the	the	DET
ap-296	128	16	fuzzy	fuzzy	ADJ
ap-296	128	17	model	model	NOUN
ap-296	128	18	has	have	VERB
ap-296	128	19	k	k	PROPN
ap-296	128	20	rules	rule	NOUN
ap-296	128	21	from	from	ADP
ap-296	128	22	(	(	PUNCT
ap-296	128	23	m)n	m)n	X
ap-296	128	24	rules	rule	NOUN
ap-296	128	25	theoretically	theoretically	ADV
ap-296	128	26	possible	possible	ADJ
ap-296	128	27	.	.	PUNCT
ap-296	129	1	this	this	DET
ap-296	129	2	means	mean	VERB
ap-296	129	3	have	have	VERB
ap-296	129	4	k	k	PROPN
ap-296	129	5	rule	rule	NOUN
ap-296	129	6	weights	weight	NOUN
ap-296	129	7	;	;	PUNCT
ap-296	129	8	w	w	NOUN
ap-296	129	9	and	and	CCONJ
ap-296	129	10	k	k	PROPN
ap-296	129	11	centroids	centroid	NOUN
ap-296	129	12	represented	represent	VERB
ap-296	129	13	by	by	ADP
ap-296	129	14	singletons	singleton	NOUN
ap-296	129	15	b.	b.	PROPN
ap-296	130	1	thus	thus	ADV
ap-296	130	2	a	a	DET
ap-296	130	3	total	total	NOUN
ap-296	130	4	of	of	ADP
ap-296	130	5	2(m	2(m	NUM
ap-296	130	6	×	×	NOUN
ap-296	130	7	n+k	n+k	NUM
ap-296	130	8	)	)	PUNCT
ap-296	130	9	parameters	parameter	NOUN
ap-296	130	10	(	(	PUNCT
ap-296	130	11	2×mmembership_functions×	2×mmembership_functions×	DET
ap-296	130	12	nvariables+kweights+kcentroids	nvariables+kweights+kcentroid	NOUN
ap-296	130	13	)	)	PUNCT
ap-296	130	14	need	need	VERB
ap-296	130	15	to	to	PART
ap-296	130	16	be	be	AUX
ap-296	130	17	optimized	optimize	VERB
ap-296	130	18	using	use	VERB
ap-296	130	19	laga	laga	NOUN
ap-296	130	20	.	.	PUNCT
ap-296	131	1	the	the	DET
ap-296	131	2	coded	code	VERB
ap-296	131	3	parameters	parameter	NOUN
ap-296	131	4	of	of	ADP
ap-296	131	5	©	©	PROPN
ap-296	131	6	czech	czech	PROPN
ap-296	131	7	technical	technical	PROPN
ap-296	131	8	university	university	PROPN
ap-296	131	9	publishing	publishing	NOUN
ap-296	131	10	house	house	NOUN
ap-296	131	11	http://ctn.cvut.cz/ap/	http://ctn.cvut.cz/ap/	PROPN
ap-296	131	12	71	71	NUM
ap-296	131	13	acta	acta	PROPN
ap-296	131	14	polytechnica	polytechnica	PROPN
ap-296	131	15	vol	vol	NOUN
ap-296	131	16	.	.	PUNCT
ap-296	132	1	41	41	NUM
ap-296	132	2	no	no	NOUN
ap-296	132	3	.	.	PUNCT
ap-296	133	1	6/2001	6/2001	NUM
ap-296	133	2	fig	fig	NOUN
ap-296	133	3	.	.	PUNCT
ap-296	134	1	2	2	NUM
ap-296	134	2	:	:	PUNCT
ap-296	134	3	upper	upper	ADJ
ap-296	134	4	and	and	CCONJ
ap-296	134	5	lower	low	ADJ
ap-296	134	6	boundaries	boundary	NOUN
ap-296	134	7	of	of	ADP
ap-296	134	8	spread	spread	NOUN
ap-296	134	9	,	,	PUNCT
ap-296	134	10	�	�	PROPN
ap-296	134	11	using	use	VERB
ap-296	134	12	a	a	DET
ap-296	134	13	2nd	2nd	ADJ
ap-296	134	14	order	order	NOUN
ap-296	134	15	fuzzy	fuzzy	ADJ
ap-296	134	16	set	set	VERB
ap-296	134	17	fig	fig	NOUN
ap-296	134	18	.	.	PUNCT
ap-296	135	1	3	3	NUM
ap-296	135	2	:	:	PUNCT
ap-296	135	3	upper	upper	ADJ
ap-296	135	4	and	and	CCONJ
ap-296	135	5	lower	low	ADJ
ap-296	135	6	boundaries	boundary	NOUN
ap-296	135	7	of	of	ADP
ap-296	135	8	center	center	NOUN
ap-296	135	9	,	,	PUNCT
ap-296	135	10	c	c	NOUN
ap-296	135	11	using	use	VERB
ap-296	135	12	a	a	DET
ap-296	135	13	2nd	2nd	ADJ
ap-296	135	14	order	order	NOUN
ap-296	135	15	fuzzy	fuzzy	ADJ
ap-296	135	16	set	set	VERB
ap-296	135	17	flnn	flnn	NOUN
ap-296	135	18	are	be	AUX
ap-296	135	19	arranged	arrange	VERB
ap-296	135	20	as	as	SCONJ
ap-296	135	21	shown	show	VERB
ap-296	135	22	in	in	ADP
ap-296	135	23	table	table	NOUN
ap-296	135	24	1	1	NUM
ap-296	135	25	to	to	PART
ap-296	135	26	form	form	VERB
ap-296	135	27	the	the	DET
ap-296	135	28	chromosome	chromosome	NOUN
ap-296	135	29	of	of	ADP
ap-296	135	30	the	the	DET
ap-296	135	31	population	population	NOUN
ap-296	135	32	.	.	PUNCT
ap-296	136	1	5.3	5.3	NUM
ap-296	136	2	selection	selection	NOUN
ap-296	136	3	function	function	VERB
ap-296	136	4	the	the	DET
ap-296	136	5	selection	selection	NOUN
ap-296	136	6	strategy	strategy	NOUN
ap-296	136	7	decides	decide	VERB
ap-296	136	8	how	how	SCONJ
ap-296	136	9	to	to	PART
ap-296	136	10	select	select	VERB
ap-296	136	11	individuals	individual	NOUN
ap-296	136	12	to	to	PART
ap-296	136	13	be	be	AUX
ap-296	136	14	parents	parent	NOUN
ap-296	136	15	for	for	ADP
ap-296	136	16	new	new	ADJ
ap-296	136	17	‘	'	PUNCT
ap-296	136	18	childs	child	NOUN
ap-296	136	19	’	'	PUNCT
ap-296	136	20	.	.	PUNCT
ap-296	137	1	usually	usually	ADV
ap-296	137	2	the	the	DET
ap-296	137	3	selection	selection	NOUN
ap-296	137	4	applies	apply	VERB
ap-296	137	5	some	some	DET
ap-296	137	6	selection	selection	NOUN
ap-296	137	7	pressure	pressure	NOUN
ap-296	137	8	by	by	ADP
ap-296	137	9	favoring	favor	VERB
ap-296	137	10	individuals	individual	NOUN
ap-296	137	11	with	with	ADP
ap-296	137	12	better	well	ADJ
ap-296	137	13	fitness	fitness	NOUN
ap-296	137	14	.	.	PUNCT
ap-296	138	1	after	after	ADP
ap-296	138	2	procreation	procreation	NOUN
ap-296	138	3	,	,	PUNCT
ap-296	138	4	the	the	DET
ap-296	138	5	suitable	suitable	ADJ
ap-296	138	6	population	population	NOUN
ap-296	138	7	consists	consist	VERB
ap-296	138	8	for	for	ADP
ap-296	138	9	example	example	NOUN
ap-296	138	10	of	of	ADP
ap-296	138	11	l	l	NOUN
ap-296	138	12	chromosomes	chromosome	NOUN
ap-296	138	13	,	,	PUNCT
ap-296	138	14	which	which	PRON
ap-296	138	15	are	be	AUX
ap-296	138	16	all	all	PRON
ap-296	138	17	initially	initially	ADV
ap-296	138	18	randomized	randomize	VERB
ap-296	138	19	.	.	PUNCT
ap-296	139	1	each	each	DET
ap-296	139	2	chromosome	chromosome	NOUN
ap-296	139	3	has	have	AUX
ap-296	139	4	been	be	AUX
ap-296	139	5	evaluated	evaluate	VERB
ap-296	139	6	and	and	CCONJ
ap-296	139	7	associated	associate	VERB
ap-296	139	8	with	with	ADP
ap-296	139	9	fitness	fitness	NOUN
ap-296	139	10	,	,	PUNCT
ap-296	139	11	the	the	DET
ap-296	139	12	current	current	ADJ
ap-296	139	13	population	population	NOUN
ap-296	139	14	undergoes	undergo	VERB
ap-296	139	15	the	the	DET
ap-296	139	16	reproduction	reproduction	NOUN
ap-296	139	17	process	process	NOUN
ap-296	139	18	to	to	PART
ap-296	139	19	create	create	VERB
ap-296	139	20	the	the	DET
ap-296	139	21	next	next	ADJ
ap-296	139	22	population	population	NOUN
ap-296	139	23	,	,	PUNCT
ap-296	139	24	and	and	CCONJ
ap-296	139	25	the	the	DET
ap-296	139	26	“	"	PUNCT
ap-296	139	27	roulette	roulette	NOUN
ap-296	139	28	wheel	wheel	NOUN
ap-296	139	29	”	"	PUNCT
ap-296	139	30	selection	selection	NOUN
ap-296	139	31	scheme	scheme	NOUN
ap-296	139	32	is	be	AUX
ap-296	139	33	used	use	VERB
ap-296	139	34	to	to	PART
ap-296	139	35	determine	determine	VERB
ap-296	139	36	the	the	DET
ap-296	139	37	member	member	NOUN
ap-296	139	38	of	of	ADP
ap-296	139	39	the	the	DET
ap-296	139	40	new	new	ADJ
ap-296	139	41	population	population	NOUN
ap-296	139	42	.	.	PUNCT
ap-296	140	1	the	the	DET
ap-296	140	2	chance	chance	NOUN
ap-296	140	3	on	on	ADP
ap-296	140	4	the	the	DET
ap-296	140	5	roulette	roulette	NOUN
ap-296	140	6	-	-	PUNCT
ap-296	140	7	wheel	wheel	NOUN
ap-296	140	8	is	be	AUX
ap-296	140	9	adaptive	adaptive	ADJ
ap-296	140	10	and	and	CCONJ
ap-296	140	11	is	be	AUX
ap-296	140	12	given	give	VERB
ap-296	140	13	as	as	ADP
ap-296	140	14	pl	pl	PROPN
ap-296	140	15	/	/	SYM
ap-296	140	16	pl	pl	PROPN
ap-296	140	17	,	,	PUNCT
ap-296	140	18	where	where	SCONJ
ap-296	140	19	�	�	PROPN
ap-296	140	20	�	�	PROPN
ap-296	140	21	p	p	PROPN
ap-296	140	22	j	j	PROPN
ap-296	140	23	l	l	NOUN
ap-296	140	24	ll	ll	AUX
ap-296	140	25	l	l	PROPN
ap-296	140	26	�	�	PROPN
ap-296	140	27	�	�	PROPN
ap-296	140	28	�	�	PROPN
ap-296	140	29	�	�	PROPN
ap-296	140	30	�	�	PROPN
ap-296	140	31	�	�	PROPN
ap-296	140	32	�	�	PROPN
ap-296	140	33	1	1	NUM
ap-296	140	34	1	1	NUM
ap-296	140	35	,	,	PUNCT
ap-296	140	36	,	,	PUNCT
ap-296	140	37	,	,	PUNCT
ap-296	140	38	�	�	PROPN
ap-296	140	39	and	and	CCONJ
ap-296	140	40	jl	jl	NOUN
ap-296	140	41	is	be	AUX
ap-296	140	42	the	the	DET
ap-296	140	43	performance	performance	NOUN
ap-296	140	44	of	of	ADP
ap-296	140	45	the	the	DET
ap-296	140	46	model	model	NOUN
ap-296	140	47	encoded	encode	VERB
ap-296	140	48	in	in	ADP
ap-296	140	49	the	the	DET
ap-296	140	50	chromosome	chromosome	NOUN
ap-296	140	51	measured	measure	VERB
ap-296	140	52	in	in	ADP
ap-296	140	53	terms	term	NOUN
ap-296	140	54	of	of	ADP
ap-296	140	55	the	the	DET
ap-296	140	56	normalized	normalize	VERB
ap-296	140	57	root	root	NOUN
ap-296	140	58	mean	mean	VERB
ap-296	140	59	square	square	ADJ
ap-296	140	60	error	error	NOUN
ap-296	140	61	(	(	PUNCT
ap-296	140	62	rmse	rmse	PROPN
ap-296	140	63	):	):	PUNCT
ap-296	141	1	�	�	PROPN
ap-296	141	2	�	�	PROPN
ap-296	141	3	�	�	PROPN
ap-296	141	4	�	�	PROPN
ap-296	141	5	j	j	PROPN
ap-296	141	6	y	y	PROPN
ap-296	141	7	x	x	PROPN
ap-296	141	8	y	y	VERB
ap-296	141	9	y	y	PROPN
ap-296	141	10	y	y	PROPN
ap-296	141	11	i	i	PRON
ap-296	141	12	n	n	VERB
ap-296	141	13	i	i	PRON
ap-296	141	14	n	n	CCONJ
ap-296	141	15	�	�	PROPN
ap-296	141	16	�	�	PROPN
ap-296	141	17	�	�	PROPN
ap-296	141	18	�	�	PROPN
ap-296	141	19	�	�	PROPN
ap-296	141	20	�	�	PROPN
ap-296	141	21	�	�	PROPN
ap-296	142	1	i	i	PRON
ap-296	142	2	i	i	PRON
ap-296	143	1	i	i	PRON
ap-296	143	2	,	,	PUNCT
ap-296	143	3	�	�	PROPN
ap-296	143	4	�	�	PROPN
ap-296	143	5	2	2	NUM
ap-296	143	6	1	1	NUM
ap-296	143	7	2	2	NUM
ap-296	143	8	1	1	NUM
ap-296	143	9	�	�	NOUN
ap-296	143	10	y	y	PROPN
ap-296	143	11	where	where	SCONJ
ap-296	143	12	n	n	PRON
ap-296	143	13	is	be	AUX
ap-296	143	14	the	the	DET
ap-296	143	15	number	number	NOUN
ap-296	143	16	of	of	ADP
ap-296	143	17	point	point	NOUN
ap-296	143	18	samples	sample	NOUN
ap-296	143	19	,	,	PUNCT
ap-296	143	20	�	�	PROPN
ap-296	143	21	is	be	AUX
ap-296	143	22	the	the	DET
ap-296	143	23	required	require	VERB
ap-296	143	24	parameters	parameter	NOUN
ap-296	143	25	to	to	PART
ap-296	143	26	be	be	AUX
ap-296	143	27	optimized	optimize	VERB
ap-296	143	28	,	,	PUNCT
ap-296	143	29	y	y	PROPN
ap-296	143	30	is	be	AUX
ap-296	143	31	the	the	DET
ap-296	143	32	true	true	ADJ
ap-296	143	33	output	output	NOUN
ap-296	143	34	,	,	PUNCT
ap-296	143	35	y	y	PROPN
ap-296	143	36	n	n	CCONJ
ap-296	143	37	y	y	PROPN
ap-296	143	38	i	i	PROPN
ap-296	143	39	n	n	PROPN
ap-296	143	40	�	�	PROPN
ap-296	143	41	�	�	PROPN
ap-296	143	42	�	�	PROPN
ap-296	143	43	1	1	NUM
ap-296	143	44	1	1	NUM
ap-296	143	45	,	,	PUNCT
ap-296	143	46	72	72	NUM
ap-296	143	47	acta	acta	PROPN
ap-296	143	48	polytechnica	polytechnica	PROPN
ap-296	143	49	vol	vol	NOUN
ap-296	143	50	.	.	PUNCT
ap-296	144	1	41	41	NUM
ap-296	144	2	no	no	NOUN
ap-296	144	3	.	.	PUNCT
ap-296	145	1	6/2001	6/2001	NUM
ap-296	145	2	chromosome	chromosome	NOUN
ap-296	145	3	sub	sub	NOUN
ap-296	145	4	-	-	NOUN
ap-296	145	5	chromosome	chromosome	NOUN
ap-296	145	6	of	of	ADP
ap-296	145	7	inputs	input	NOUN
ap-296	145	8	sub	sub	NOUN
ap-296	145	9	-	-	NOUN
ap-296	145	10	chromosome	chromosome	NOUN
ap-296	145	11	of	of	ADP
ap-296	145	12	rule	rule	NOUN
ap-296	145	13	weights	weight	NOUN
ap-296	145	14	sub	sub	NOUN
ap-296	145	15	-	-	NOUN
ap-296	145	16	chromosome	chromosome	NOUN
ap-296	145	17	of	of	ADP
ap-296	145	18	rule	rule	NOUN
ap-296	145	19	consequents	consequent	NOUN
ap-296	145	20	x1	x1	PROPN
ap-296	145	21	,	,	PUNCT
ap-296	145	22	……	……	NOUN
ap-296	145	23	……	……	NOUN
ap-296	145	24	,	,	PUNCT
ap-296	145	25	xn	xn	PROPN
ap-296	145	26	w1	w1	PROPN
ap-296	145	27	,	,	PUNCT
ap-296	145	28	…	…	PUNCT
ap-296	145	29	…	…	PUNCT
ap-296	145	30	…	…	PUNCT
ap-296	145	31	,	,	PUNCT
ap-296	145	32	wk	wk	X
ap-296	145	33	b1	b1	PROPN
ap-296	145	34	,	,	PUNCT
ap-296	145	35	…	…	PUNCT
ap-296	145	36	……	……	NOUN
ap-296	145	37	,	,	PUNCT
ap-296	145	38	bk	bk	VERB
ap-296	145	39	parameters	parameter	NOUN
ap-296	145	40	c1	c1	PROPN
ap-296	145	41	,	,	PUNCT
ap-296	145	42	�	�	PROPN
ap-296	145	43	1	1	NUM
ap-296	145	44	,	,	PUNCT
ap-296	145	45	…	…	PUNCT
ap-296	145	46	…	…	PUNCT
ap-296	145	47	…	…	PUNCT
ap-296	145	48	,	,	PUNCT
ap-296	145	49	cn	cn	PROPN
ap-296	145	50	,	,	PUNCT
ap-296	145	51	�	�	PROPN
ap-296	145	52	n	n	PART
ap-296	145	53	w1	w1	NOUN
ap-296	145	54	,	,	PUNCT
ap-296	145	55	…	…	PUNCT
ap-296	145	56	…	…	PUNCT
ap-296	145	57	…	…	PUNCT
ap-296	145	58	,	,	PUNCT
ap-296	145	59	wk	wk	X
ap-296	145	60	b1	b1	PROPN
ap-296	145	61	,	,	PUNCT
ap-296	145	62	…	…	PUNCT
ap-296	145	63	…	…	PUNCT
ap-296	145	64	…	…	PUNCT
ap-296	145	65	,	,	PUNCT
ap-296	145	66	bk	bk	VERB
ap-296	145	67	2(m	2(m	NUM
ap-296	145	68	×	×	NOUN
ap-296	145	69	n+k	n+k	NUM
ap-296	145	70	)	)	PUNCT
ap-296	145	71	(	(	PUNCT
ap-296	145	72	2	2	NUM
ap-296	145	73	m	m	NOUN
ap-296	145	74	×	×	NOUN
ap-296	145	75	n	n	CCONJ
ap-296	145	76	)	)	PUNCT
ap-296	145	77	k	k	PROPN
ap-296	145	78	k	k	PROPN
ap-296	145	79	gene	gene	NOUN
ap-296	145	80	1000000000	1000000000	NUM
ap-296	145	81	…	…	SYM
ap-296	145	82	0110100011	0110100011	NUM
ap-296	145	83	0100111111	0100111111	NUM
ap-296	145	84	…	…	SYM
ap-296	145	85	0111001010	0111001010	NUM
ap-296	145	86	…	…	PUNCT
ap-296	145	87	table	table	NOUN
ap-296	145	88	1	1	NUM
ap-296	145	89	:	:	PUNCT
ap-296	145	90	coded	code	VERB
ap-296	145	91	parameters	parameter	NOUN
ap-296	145	92	of	of	ADP
ap-296	145	93	flnn	flnn	NOUN
ap-296	145	94	actual	actual	ADJ
ap-296	145	95	model	model	NOUN
ap-296	145	96	fuzzy	fuzzy	PROPN
ap-296	145	97	model	model	PROPN
ap-296	145	98	y	y	PROPN
ap-296	145	99	�	�	PROPN
ap-296	145	100	;	;	PUNCT
ap-296	145	101	xf	xf	PROPN
ap-296	145	102	�	�	PROPN
ap-296	145	103	+	+	CCONJ
ap-296	145	104	laga	laga	PROPN
ap-296	145	105	y	y	PROPN
ap-296	145	106	�	�	PROPN
ap-296	145	107	y	y	PROPN
ap-296	145	108	�	�	PROPN
ap-296	145	109	fig	fig	PROPN
ap-296	145	110	.	.	PUNCT
ap-296	146	1	4	4	NUM
ap-296	146	2	:	:	PUNCT
ap-296	146	3	block	block	NOUN
ap-296	146	4	diagram	diagram	NOUN
ap-296	146	5	of	of	ADP
ap-296	146	6	parameters	parameter	NOUN
ap-296	146	7	identification	identification	NOUN
ap-296	146	8	start	start	VERB
ap-296	146	9	initialization	initialization	NOUN
ap-296	146	10	performance	performance	NOUN
ap-296	146	11	evaluation	evaluation	NOUN
ap-296	146	12	parameters	parameter	NOUN
ap-296	146	13	flnn	flnn	VERB
ap-296	146	14	model	model	NOUN
ap-296	146	15	laga	laga	PROPN
ap-296	146	16	x	x	PROPN
ap-296	146	17	y	y	PROPN
ap-296	146	18	rmse	rmse	NOUN
ap-296	146	19	>	>	X
ap-296	146	20	tolerance	tolerance	NOUN
ap-296	146	21	mutation	mutation	NOUN
ap-296	146	22	reproduction	reproduction	NOUN
ap-296	146	23	crossover	crossover	NOUN
ap-296	146	24	stoprecord	stoprecord	NOUN
ap-296	146	25	required	require	VERB
ap-296	146	26	information	information	NOUN
ap-296	146	27	yes	yes	INTJ
ap-296	146	28	no	no	DET
ap-296	146	29	randomly	randomly	ADV
ap-296	146	30	generated	generate	VERB
ap-296	146	31	chromosomes	chromosome	NOUN
ap-296	146	32	fig	fig	NOUN
ap-296	146	33	.	.	PUNCT
ap-296	147	1	5	5	NUM
ap-296	147	2	:	:	PUNCT
ap-296	147	3	block	block	NOUN
ap-296	147	4	diagram	diagram	NOUN
ap-296	147	5	for	for	ADP
ap-296	147	6	the	the	DET
ap-296	147	7	laga	laga	NOUN
ap-296	147	8	optimization	optimization	NOUN
ap-296	147	9	process	process	NOUN
ap-296	147	10	and	and	CCONJ
ap-296	147	11	�	�	PROPN
ap-296	147	12	y	y	PROPN
ap-296	147	13	is	be	AUX
ap-296	147	14	the	the	DET
ap-296	147	15	model	model	NOUN
ap-296	147	16	output	output	NOUN
ap-296	147	17	,	,	PUNCT
ap-296	147	18	as	as	SCONJ
ap-296	147	19	shown	show	VERB
ap-296	147	20	in	in	ADP
ap-296	147	21	fig	fig	NOUN
ap-296	147	22	.	.	PUNCT
ap-296	148	1	4	4	X
ap-296	148	2	.	.	X
ap-296	148	3	the	the	DET
ap-296	148	4	inverse	inverse	NOUN
ap-296	148	5	of	of	ADP
ap-296	148	6	the	the	DET
ap-296	148	7	selection	selection	NOUN
ap-296	148	8	function	function	NOUN
ap-296	148	9	is	be	AUX
ap-296	148	10	used	use	VERB
ap-296	148	11	to	to	PART
ap-296	148	12	select	select	VERB
ap-296	148	13	chromosomes	chromosome	NOUN
ap-296	148	14	for	for	ADP
ap-296	148	15	deletion	deletion	NOUN
ap-296	148	16	.	.	PUNCT
ap-296	149	1	5.4	5.4	NUM
ap-296	149	2	crossover	crossover	NOUN
ap-296	149	3	and	and	CCONJ
ap-296	149	4	mutation	mutation	NOUN
ap-296	149	5	operators	operator	NOUN
ap-296	149	6	the	the	DET
ap-296	149	7	mating	mating	NOUN
ap-296	149	8	pool	pool	NOUN
ap-296	149	9	is	be	AUX
ap-296	149	10	formed	form	VERB
ap-296	149	11	,	,	PUNCT
ap-296	149	12	and	and	CCONJ
ap-296	149	13	crossover	crossover	NOUN
ap-296	149	14	is	be	AUX
ap-296	149	15	applied	apply	VERB
ap-296	149	16	and	and	CCONJ
ap-296	149	17	followed	follow	VERB
ap-296	149	18	by	by	ADP
ap-296	149	19	a	a	DET
ap-296	149	20	mutation	mutation	NOUN
ap-296	149	21	operation	operation	NOUN
ap-296	149	22	following	follow	VERB
ap-296	149	23	the	the	DET
ap-296	149	24	laga	laga	NOUN
ap-296	149	25	approach	approach	NOUN
ap-296	149	26	.	.	PUNCT
ap-296	150	1	finally	finally	ADV
ap-296	150	2	,	,	PUNCT
ap-296	150	3	after	after	ADP
ap-296	150	4	these	these	DET
ap-296	150	5	three	three	NUM
ap-296	150	6	operations	operation	NOUN
ap-296	150	7	,	,	PUNCT
ap-296	150	8	the	the	DET
ap-296	150	9	overall	overall	ADJ
ap-296	150	10	fitness	fitness	NOUN
ap-296	150	11	of	of	ADP
ap-296	150	12	the	the	DET
ap-296	150	13	population	population	NOUN
ap-296	150	14	is	be	AUX
ap-296	150	15	improved	improve	VERB
ap-296	150	16	.	.	PUNCT
ap-296	151	1	the	the	DET
ap-296	151	2	procedure	procedure	NOUN
ap-296	151	3	is	be	AUX
ap-296	151	4	repeated	repeat	VERB
ap-296	151	5	until	until	SCONJ
ap-296	151	6	the	the	DET
ap-296	151	7	termination	termination	NOUN
ap-296	151	8	condition	condition	NOUN
ap-296	151	9	is	be	AUX
ap-296	151	10	reached	reach	VERB
ap-296	151	11	.	.	PUNCT
ap-296	152	1	the	the	DET
ap-296	152	2	termination	termination	NOUN
ap-296	152	3	condition	condition	NOUN
ap-296	152	4	is	be	AUX
ap-296	152	5	the	the	DET
ap-296	152	6	maximum	maximum	ADJ
ap-296	152	7	allowable	allowable	ADJ
ap-296	152	8	number	number	NOUN
ap-296	152	9	of	of	ADP
ap-296	152	10	generations	generation	NOUN
ap-296	152	11	,	,	PUNCT
ap-296	152	12	or	or	CCONJ
ap-296	152	13	a	a	DET
ap-296	152	14	certain	certain	ADJ
ap-296	152	15	value	value	NOUN
ap-296	152	16	of	of	ADP
ap-296	152	17	(	(	PUNCT
ap-296	152	18	rmse	rmse	NOUN
ap-296	152	19	)	)	PUNCT
ap-296	152	20	required	require	VERB
ap-296	152	21	to	to	PART
ap-296	152	22	be	be	AUX
ap-296	152	23	reached	reach	VERB
ap-296	152	24	.	.	PUNCT
ap-296	153	1	6	6	NUM
ap-296	153	2	applications	application	NOUN
ap-296	153	3	6.1	6.1	NUM
ap-296	153	4	modeling	model	VERB
ap-296	153	5	the	the	DET
ap-296	153	6	mackey	mackey	NOUN
ap-296	153	7	-	-	PUNCT
ap-296	153	8	glass	glass	NOUN
ap-296	153	9	process	process	NOUN
ap-296	153	10	the	the	DET
ap-296	153	11	process	process	NOUN
ap-296	153	12	used	use	VERB
ap-296	153	13	as	as	ADP
ap-296	153	14	an	an	DET
ap-296	153	15	object	object	NOUN
ap-296	153	16	of	of	ADP
ap-296	153	17	modeling	modeling	NOUN
ap-296	153	18	is	be	AUX
ap-296	153	19	defined	define	VERB
ap-296	153	20	by	by	ADP
ap-296	153	21	the	the	DET
ap-296	153	22	chaotic	chaotic	ADJ
ap-296	153	23	mackey	mackey	NOUN
ap-296	153	24	-	-	PUNCT
ap-296	153	25	glass	glass	NOUN
ap-296	153	26	differential	differential	NOUN
ap-296	153	27	delay	delay	NOUN
ap-296	153	28	equation	equation	NOUN
ap-296	153	29	[	[	X
ap-296	153	30	8	8	NUM
ap-296	153	31	]	]	SYM
ap-296	153	32	:	:	PUNCT
ap-296	153	33	�	�	PROPN
ap-296	153	34	�	�	PROPN
ap-296	153	35	�	�	PROPN
ap-296	153	36	�	�	PROPN
ap-296	153	37	�	�	PROPN
ap-296	153	38	�	�	PROPN
ap-296	153	39	�	�	PROPN
ap-296	153	40	�	�	PROPN
ap-296	153	41	x	x	PROPN
ap-296	153	42	t	t	PROPN
ap-296	153	43	x	x	SYM
ap-296	153	44	t	t	NOUN
ap-296	153	45	x	x	SYM
ap-296	153	46	t	t	PROPN
ap-296	153	47	x	x	SYM
ap-296	153	48	t	t	PROPN
ap-296	153	49	�	�	PROPN
ap-296	153	50	�	�	PROPN
ap-296	153	51	�	�	PROPN
ap-296	153	52	�	�	PROPN
ap-296	153	53	�	�	PROPN
ap-296	153	54	02	02	NUM
ap-296	153	55	1	1	NUM
ap-296	153	56	0110	0110	NUM
ap-296	153	57	.	.	PUNCT
ap-296	153	58	.	.	PUNCT
ap-296	154	1	�	�	PROPN
ap-296	154	2	�	�	PROPN
ap-296	154	3	(	(	PUNCT
ap-296	154	4	15	15	NUM
ap-296	154	5	)	)	PUNCT
ap-296	154	6	the	the	DET
ap-296	154	7	prediction	prediction	NOUN
ap-296	154	8	of	of	ADP
ap-296	154	9	future	future	ADJ
ap-296	154	10	values	value	NOUN
ap-296	154	11	of	of	ADP
ap-296	154	12	this	this	DET
ap-296	154	13	time	time	NOUN
ap-296	154	14	series	series	NOUN
ap-296	154	15	is	be	AUX
ap-296	154	16	a	a	DET
ap-296	154	17	benchmark	benchmark	NOUN
ap-296	154	18	problem	problem	NOUN
ap-296	154	19	,	,	PUNCT
ap-296	154	20	which	which	PRON
ap-296	154	21	has	have	AUX
ap-296	154	22	been	be	AUX
ap-296	154	23	considered	consider	VERB
ap-296	154	24	by	by	ADP
ap-296	154	25	a	a	DET
ap-296	154	26	number	number	NOUN
ap-296	154	27	of	of	ADP
ap-296	154	28	connectionist	connectionist	NOUN
ap-296	154	29	researchers	researcher	NOUN
ap-296	154	30	.	.	PUNCT
ap-296	155	1	a	a	DET
ap-296	155	2	time	time	NOUN
ap-296	155	3	window	window	NOUN
ap-296	155	4	of	of	ADP
ap-296	155	5	the	the	DET
ap-296	155	6	process	process	NOUN
ap-296	155	7	behavior	behavior	NOUN
ap-296	155	8	is	be	AUX
ap-296	155	9	shown	show	VERB
ap-296	155	10	in	in	ADP
ap-296	155	11	fig	fig	NOUN
ap-296	155	12	.	.	PUNCT
ap-296	156	1	6	6	NUM
ap-296	156	2	.	.	X
ap-296	156	3	the	the	DET
ap-296	156	4	sampling	sample	VERB
ap-296	156	5	period	period	NOUN
ap-296	156	6	used	use	VERB
ap-296	156	7	in	in	ADP
ap-296	156	8	the	the	DET
ap-296	156	9	numerical	numerical	ADJ
ap-296	156	10	study	study	NOUN
ap-296	156	11	is	be	AUX
ap-296	156	12	set	set	VERB
ap-296	156	13	to	to	ADP
ap-296	156	14	0.1	0.1	NUM
ap-296	156	15	,	,	PUNCT
ap-296	156	16	initial	initial	ADJ
ap-296	156	17	condition	condition	NOUN
ap-296	156	18	x(0	x(0	PROPN
ap-296	156	19	)	)	PUNCT
ap-296	156	20	�	�	PROPN
ap-296	156	21	1.2	1.2	NUM
ap-296	156	22	and	and	CCONJ
ap-296	156	23	time	time	NOUN
ap-296	156	24	delay	delay	PROPN
ap-296	156	25	�	�	PROPN
ap-296	156	26	�	�	PROPN
ap-296	156	27	17	17	NUM
ap-296	156	28	.	.	PUNCT
ap-296	157	1	in	in	ADP
ap-296	157	2	according	accord	VERB
ap-296	157	3	with	with	ADP
ap-296	157	4	[	[	X
ap-296	157	5	8	8	NUM
ap-296	157	6	]	]	PUNCT
ap-296	157	7	,	,	PUNCT
ap-296	157	8	we	we	PRON
ap-296	157	9	use	use	VERB
ap-296	157	10	the	the	DET
ap-296	157	11	samples	sample	NOUN
ap-296	157	12	of	of	ADP
ap-296	157	13	x(t	x(t	PROPN
ap-296	157	14	�	�	NOUN
ap-296	157	15	18	18	NUM
ap-296	157	16	)	)	PUNCT
ap-296	157	17	,	,	PUNCT
ap-296	157	18	x(t	x(t	PROPN
ap-296	157	19	�	�	NOUN
ap-296	157	20	12	12	NUM
ap-296	157	21	)	)	PUNCT
ap-296	157	22	,	,	PUNCT
ap-296	157	23	x(t	x(t	PROPN
ap-296	157	24	�	�	PROPN
ap-296	157	25	6	6	NUM
ap-296	157	26	)	)	PUNCT
ap-296	157	27	and	and	CCONJ
ap-296	157	28	x(t	x(t	PROPN
ap-296	157	29	)	)	PUNCT
ap-296	157	30	to	to	PART
ap-296	157	31	predict	predict	VERB
ap-296	157	32	x(t+6	x(t+6	PROPN
ap-296	157	33	)	)	PUNCT
ap-296	157	34	.	.	PUNCT
ap-296	158	1	for	for	ADP
ap-296	158	2	the	the	DET
ap-296	158	3	chaotic	chaotic	ADJ
ap-296	158	4	system	system	NOUN
ap-296	158	5	,	,	PUNCT
ap-296	158	6	the	the	DET
ap-296	158	7	model	model	NOUN
ap-296	158	8	has	have	VERB
ap-296	158	9	four	four	NUM
ap-296	158	10	input	input	NOUN
ap-296	158	11	variables	variable	NOUN
ap-296	158	12	x(t	x(t	PROPN
ap-296	158	13	�	�	PROPN
ap-296	158	14	18	18	NUM
ap-296	158	15	)	)	PUNCT
ap-296	158	16	,	,	PUNCT
ap-296	158	17	x(t	x(t	PROPN
ap-296	158	18	�	�	NOUN
ap-296	158	19	12	12	NUM
ap-296	158	20	)	)	PUNCT
ap-296	158	21	,	,	PUNCT
ap-296	158	22	x(t	x(t	PROPN
ap-296	158	23	�	�	PROPN
ap-296	158	24	6	6	NUM
ap-296	158	25	)	)	PUNCT
ap-296	158	26	and	and	CCONJ
ap-296	158	27	x(t	x(t	PROPN
ap-296	158	28	)	)	PUNCT
ap-296	158	29	,	,	PUNCT
ap-296	158	30	and	and	CCONJ
ap-296	158	31	a	a	DET
ap-296	158	32	single	single	ADJ
ap-296	158	33	output	output	NOUN
ap-296	158	34	x(t+6	x(t+6	PUNCT
ap-296	158	35	)	)	PUNCT
ap-296	158	36	.	.	PUNCT
ap-296	159	1	the	the	DET
ap-296	159	2	values	value	NOUN
ap-296	159	3	of	of	ADP
ap-296	159	4	every	every	DET
ap-296	159	5	input	input	NOUN
ap-296	159	6	variable	variable	NOUN
ap-296	159	7	are	be	AUX
ap-296	159	8	classified	classify	VERB
ap-296	159	9	into	into	ADP
ap-296	159	10	three	three	NUM
ap-296	159	11	reference	reference	NOUN
ap-296	159	12	fuzzy	fuzzy	ADJ
ap-296	159	13	sets	set	NOUN
ap-296	159	14	.	.	PUNCT
ap-296	160	1	every	every	DET
ap-296	160	2	reference	reference	NOUN
ap-296	160	3	fuzzy	fuzzy	ADJ
ap-296	160	4	set	set	NOUN
ap-296	160	5	is	be	AUX
ap-296	160	6	described	describe	VERB
ap-296	160	7	by	by	ADP
ap-296	160	8	a	a	DET
ap-296	160	9	gaussian	gaussian	ADJ
ap-296	160	10	membership	membership	NOUN
ap-296	160	11	function	function	NOUN
ap-296	160	12	specified	specify	VERB
ap-296	160	13	by	by	ADP
ap-296	160	14	two	two	NUM
ap-296	160	15	parameters	parameter	NOUN
ap-296	160	16	:	:	PUNCT
ap-296	160	17	center	center	NOUN
ap-296	160	18	c	c	PROPN
ap-296	160	19	and	and	CCONJ
ap-296	160	20	spread	spread	VERB
ap-296	160	21	�	�	PROPN
ap-296	160	22	,	,	PUNCT
ap-296	160	23	resulting	result	VERB
ap-296	160	24	in	in	ADP
ap-296	160	25	24	24	NUM
ap-296	160	26	parameters	parameter	NOUN
ap-296	160	27	for	for	ADP
ap-296	160	28	inputs	input	NOUN
ap-296	160	29	.	.	PUNCT
ap-296	161	1	using	use	VERB
ap-296	161	2	the	the	DET
ap-296	161	3	wang	wang	PROPN
ap-296	161	4	technique	technique	NOUN
ap-296	161	5	for	for	ADP
ap-296	161	6	generating	generate	VERB
ap-296	161	7	rules	rule	NOUN
ap-296	161	8	from	from	ADP
ap-296	161	9	the	the	DET
ap-296	161	10	given	give	VERB
ap-296	161	11	data	datum	NOUN
ap-296	161	12	[	[	X
ap-296	161	13	16	16	NUM
ap-296	161	14	]	]	PUNCT
ap-296	161	15	,	,	PUNCT
ap-296	161	16	we	we	PRON
ap-296	161	17	have	have	VERB
ap-296	161	18	25	25	NUM
ap-296	161	19	rules	rule	NOUN
ap-296	161	20	from	from	ADP
ap-296	161	21	81	81	NUM
ap-296	161	22	rules	rule	NOUN
ap-296	161	23	theoretically	theoretically	ADV
ap-296	161	24	possible	possible	ADJ
ap-296	161	25	.	.	PUNCT
ap-296	162	1	this	this	PRON
ap-296	162	2	means	mean	VERB
ap-296	162	3	we	we	PRON
ap-296	162	4	have	have	VERB
ap-296	162	5	25	25	NUM
ap-296	162	6	rule	rule	NOUN
ap-296	162	7	weights	weight	NOUN
ap-296	162	8	w	w	NOUN
ap-296	162	9	and	and	CCONJ
ap-296	162	10	25	25	NUM
ap-296	162	11	centroids	centroid	NOUN
ap-296	162	12	represented	represent	VERB
ap-296	162	13	by	by	ADP
ap-296	162	14	singletons	singleton	NOUN
ap-296	162	15	b.	b.	PROPN
ap-296	163	1	thus	thus	ADV
ap-296	163	2	a	a	DET
ap-296	163	3	total	total	NOUN
ap-296	163	4	of	of	ADP
ap-296	163	5	74	74	NUM
ap-296	163	6	parameters	parameter	NOUN
ap-296	163	7	(	(	PUNCT
ap-296	163	8	2	2	NUM
ap-296	163	9	×	×	NOUN
ap-296	163	10	3membership_function×	3membership_function×	PROPN
ap-296	163	11	4variables	4variables	NUM
ap-296	163	12	+	+	NUM
ap-296	163	13	25weights	25weights	NUM
ap-296	163	14	+	+	NUM
ap-296	163	15	+25centroids	+25centroid	NOUN
ap-296	163	16	)	)	PUNCT
ap-296	163	17	need	need	VERB
ap-296	163	18	to	to	PART
ap-296	163	19	be	be	AUX
ap-296	163	20	optimized	optimize	VERB
ap-296	163	21	using	use	VERB
ap-296	163	22	laga	laga	NOUN
ap-296	163	23	.	.	PUNCT
ap-296	164	1	the	the	DET
ap-296	164	2	coded	code	VERB
ap-296	164	3	parameters	parameter	NOUN
ap-296	164	4	of	of	ADP
ap-296	164	5	flnn	flnn	NOUN
ap-296	164	6	are	be	AUX
ap-296	164	7	arranged	arrange	VERB
ap-296	164	8	as	as	SCONJ
ap-296	164	9	shown	show	VERB
ap-296	164	10	in	in	ADP
ap-296	164	11	table	table	NOUN
ap-296	164	12	1	1	NUM
ap-296	164	13	to	to	PART
ap-296	164	14	form	form	VERB
ap-296	164	15	the	the	DET
ap-296	164	16	chromosome	chromosome	NOUN
ap-296	164	17	of	of	ADP
ap-296	164	18	the	the	DET
ap-296	164	19	population	population	NOUN
ap-296	164	20	.	.	PUNCT
ap-296	165	1	to	to	PART
ap-296	165	2	describe	describe	VERB
ap-296	165	3	the	the	DET
ap-296	165	4	laga	laga	NOUN
ap-296	165	5	optimization	optimization	NOUN
ap-296	165	6	process	process	NOUN
ap-296	165	7	,	,	PUNCT
ap-296	165	8	consider	consider	VERB
ap-296	165	9	the	the	DET
ap-296	165	10	block	block	NOUN
ap-296	165	11	diagram	diagram	NOUN
ap-296	165	12	shown	show	VERB
ap-296	165	13	in	in	ADP
ap-296	165	14	fig	fig	NOUN
ap-296	165	15	.	.	PUNCT
ap-296	166	1	5	5	X
ap-296	166	2	.	.	X
ap-296	166	3	at	at	ADP
ap-296	166	4	the	the	DET
ap-296	166	5	beginning	beginning	NOUN
ap-296	166	6	of	of	ADP
ap-296	166	7	the	the	DET
ap-296	166	8	process	process	NOUN
ap-296	166	9	,	,	PUNCT
ap-296	166	10	the	the	DET
ap-296	166	11	initial	initial	ADJ
ap-296	166	12	population	population	NOUN
ap-296	166	13	comprises	comprise	VERB
ap-296	166	14	a	a	DET
ap-296	166	15	set	set	NOUN
ap-296	166	16	of	of	ADP
ap-296	166	17	chromosomes	chromosome	NOUN
ap-296	166	18	.	.	PUNCT
ap-296	167	1	every	every	DET
ap-296	167	2	chromosome	chromosome	NOUN
ap-296	167	3	has	have	VERB
ap-296	167	4	74	74	NUM
ap-296	167	5	genes	gene	NOUN
ap-296	167	6	,	,	PUNCT
ap-296	167	7	and	and	CCONJ
ap-296	167	8	every	every	DET
ap-296	167	9	gene	gene	NOUN
ap-296	167	10	has	have	VERB
ap-296	167	11	10	10	NUM
ap-296	167	12	bits	bit	NOUN
ap-296	167	13	,	,	PUNCT
ap-296	167	14	so	so	SCONJ
ap-296	167	15	the	the	DET
ap-296	167	16	chromosome	chromosome	NOUN
ap-296	167	17	length	length	NOUN
ap-296	167	18	is	be	AUX
ap-296	167	19	740	740	NUM
ap-296	167	20	bits	bit	NOUN
ap-296	167	21	.	.	PUNCT
ap-296	168	1	simulation	simulation	NOUN
ap-296	168	2	results	result	VERB
ap-296	168	3	from	from	ADP
ap-296	168	4	the	the	DET
ap-296	168	5	mackey	mackey	NOUN
ap-296	168	6	-	-	PUNCT
ap-296	168	7	glass	glass	NOUN
ap-296	168	8	time	time	NOUN
ap-296	168	9	series	series	NOUN
ap-296	168	10	x(t	x(t	PROPN
ap-296	168	11	)	)	PUNCT
ap-296	168	12	(	(	PUNCT
ap-296	168	13	15	15	NUM
ap-296	168	14	)	)	PUNCT
ap-296	168	15	,	,	PUNCT
ap-296	168	16	we	we	PRON
ap-296	168	17	extracted	extract	VERB
ap-296	168	18	3000	3000	NUM
ap-296	168	19	input	input	NOUN
ap-296	168	20	-	-	PUNCT
ap-296	168	21	output	output	NOUN
ap-296	168	22	pairs	pair	NOUN
ap-296	168	23	.	.	PUNCT
ap-296	169	1	the	the	DET
ap-296	169	2	first	first	ADJ
ap-296	169	3	1000	1000	NUM
ap-296	169	4	data	datum	NOUN
ap-296	169	5	samples	sample	NOUN
ap-296	169	6	were	be	AUX
ap-296	169	7	used	use	VERB
ap-296	169	8	to	to	PART
ap-296	169	9	build	build	VERB
ap-296	169	10	the	the	DET
ap-296	169	11	fuzzy	fuzzy	ADJ
ap-296	169	12	model	model	NOUN
ap-296	169	13	,	,	PUNCT
ap-296	169	14	while	while	SCONJ
ap-296	169	15	the	the	DET
ap-296	169	16	remaining	remain	VERB
ap-296	169	17	2000	2000	NUM
ap-296	169	18	data	datum	NOUN
ap-296	169	19	samples	sample	NOUN
ap-296	169	20	were	be	AUX
ap-296	169	21	used	use	VERB
ap-296	169	22	for	for	ADP
ap-296	169	23	model	model	NOUN
ap-296	169	24	testing	testing	NOUN
ap-296	169	25	.	.	PUNCT
ap-296	170	1	fig	fig	NOUN
ap-296	170	2	.	.	PUNCT
ap-296	171	1	7	7	NUM
ap-296	171	2	depicts	depict	VERB
ap-296	171	3	the	the	DET
ap-296	171	4	corresponding	correspond	VERB
ap-296	171	5	membership	membership	NOUN
ap-296	171	6	functions	function	NOUN
ap-296	171	7	before	before	ADP
ap-296	171	8	and	and	CCONJ
ap-296	171	9	after	after	ADP
ap-296	171	10	training	training	NOUN
ap-296	171	11	using	use	VERB
ap-296	171	12	laga	laga	NOUN
ap-296	171	13	.	.	PUNCT
ap-296	172	1	there	there	PRON
ap-296	172	2	were	be	VERB
ap-296	172	3	420	420	NUM
ap-296	172	4	generations	generation	NOUN
ap-296	172	5	,	,	PUNCT
ap-296	172	6	and	and	CCONJ
ap-296	172	7	60	60	NUM
ap-296	172	8	minutes	minute	NOUN
ap-296	172	9	of	of	ADP
ap-296	172	10	computation	computation	NOUN
ap-296	172	11	time	time	NOUN
ap-296	172	12	using	use	VERB
ap-296	172	13	matlab	matlab	PROPN
ap-296	172	14	and	and	CCONJ
ap-296	172	15	pc	pc	NOUN
ap-296	172	16	400	400	NUM
ap-296	172	17	mhz	mhz	NOUN
ap-296	172	18	with	with	ADP
ap-296	172	19	64	64	NUM
ap-296	172	20	mb	mb	NOUN
ap-296	172	21	of	of	ADP
ap-296	172	22	ram	ram	PROPN
ap-296	172	23	.	.	PUNCT
ap-296	173	1	fig	fig	NOUN
ap-296	173	2	.	.	PUNCT
ap-296	174	1	8	8	NUM
ap-296	174	2	shows	show	VERB
ap-296	174	3	that	that	SCONJ
ap-296	174	4	the	the	DET
ap-296	174	5	flnn	flnn	NOUN
ap-296	174	6	model	model	NOUN
ap-296	174	7	follows	follow	VERB
ap-296	174	8	the	the	DET
ap-296	174	9	actual	actual	ADJ
ap-296	174	10	process	process	NOUN
ap-296	174	11	.	.	PUNCT
ap-296	175	1	the	the	DET
ap-296	175	2	mse	mse	PROPN
ap-296	175	3	is	be	AUX
ap-296	175	4	0.0009	0.0009	NUM
ap-296	175	5	and	and	CCONJ
ap-296	175	6	the	the	DET
ap-296	175	7	rmse	rmse	NOUN
ap-296	175	8	is	be	AUX
ap-296	175	9	0.14	0.14	NUM
ap-296	175	10	as	as	SCONJ
ap-296	175	11	shown	show	VERB
ap-296	175	12	in	in	ADP
ap-296	175	13	fig	fig	NOUN
ap-296	175	14	.	.	PUNCT
ap-296	176	1	9	9	X
ap-296	176	2	.	.	X
ap-296	176	3	figure	figure	NOUN
ap-296	176	4	10	10	NUM
ap-296	176	5	shows	show	VERB
ap-296	176	6	the	the	DET
ap-296	176	7	shape	shape	NOUN
ap-296	176	8	of	of	ADP
ap-296	176	9	constraints	constraint	NOUN
ap-296	176	10	of	of	ADP
ap-296	176	11	membership	membership	NOUN
ap-296	176	12	functions	function	NOUN
ap-296	176	13	based	base	VERB
ap-296	176	14	on	on	ADP
ap-296	176	15	second	second	ADJ
ap-296	176	16	order	order	NOUN
ap-296	176	17	fuzzy	fuzzy	ADJ
ap-296	176	18	sets	set	NOUN
ap-296	176	19	,	,	PUNCT
ap-296	176	20	as	as	SCONJ
ap-296	176	21	explained	explain	VERB
ap-296	176	22	in	in	ADP
ap-296	176	23	section	section	NOUN
ap-296	176	24	3	3	NUM
ap-296	176	25	with	with	ADP
ap-296	176	26	�	�	NOUN
ap-296	176	27	=	=	SYM
ap-296	176	28	0.3	0.3	NUM
ap-296	176	29	.	.	PUNCT
ap-296	177	1	©	©	PROPN
ap-296	177	2	czech	czech	PROPN
ap-296	177	3	technical	technical	PROPN
ap-296	177	4	university	university	PROPN
ap-296	177	5	publishing	publishing	NOUN
ap-296	177	6	house	house	NOUN
ap-296	177	7	http://ctn.cvut.cz/ap/	http://ctn.cvut.cz/ap/	PROPN
ap-296	177	8	73	73	NUM
ap-296	177	9	acta	acta	PROPN
ap-296	177	10	polytechnica	polytechnica	PROPN
ap-296	177	11	vol	vol	NOUN
ap-296	177	12	.	.	PUNCT
ap-296	178	1	41	41	NUM
ap-296	178	2	no	no	NOUN
ap-296	178	3	.	.	PUNCT
ap-296	179	1	6/2001	6/2001	NUM
ap-296	179	2	0	0	NUM
ap-296	179	3	500	500	NUM
ap-296	179	4	1000	1000	NUM
ap-296	179	5	1500	1500	NUM
ap-296	179	6	2000	2000	NUM
ap-296	179	7	2500	2500	NUM
ap-296	179	8	3000	3000	NUM
ap-296	179	9	0.2	0.2	NUM
ap-296	179	10	0.4	0.4	NUM
ap-296	179	11	0.6	0.6	NUM
ap-296	179	12	0.8	0.8	NUM
ap-296	179	13	1	1	NUM
ap-296	179	14	1.2	1.2	NUM
ap-296	179	15	1.4	1.4	NUM
ap-296	179	16	1.6	1.6	NUM
ap-296	179	17	testing	testing	NOUN
ap-296	179	18	time	time	NOUN
ap-296	179	19	sample	sample	NOUN
ap-296	179	20	training	training	NOUN
ap-296	179	21	fig	fig	NOUN
ap-296	179	22	.	.	PUNCT
ap-296	180	1	6	6	NUM
ap-296	180	2	:	:	PUNCT
ap-296	180	3	mackey	mackey	NOUN
ap-296	180	4	-	-	PUNCT
ap-296	180	5	glass	glass	NOUN
ap-296	180	6	time	time	NOUN
ap-296	180	7	series	series	NOUN
ap-296	180	8	74	74	NUM
ap-296	180	9	©	©	PROPN
ap-296	180	10	czech	czech	PROPN
ap-296	180	11	technical	technical	PROPN
ap-296	180	12	university	university	PROPN
ap-296	180	13	publishing	publishing	NOUN
ap-296	180	14	house	house	NOUN
ap-296	180	15	http://ctn.cvut.cz/ap/	http://ctn.cvut.cz/ap/	PROPN
ap-296	180	16	acta	acta	PROPN
ap-296	180	17	polytechnica	polytechnica	PROPN
ap-296	180	18	vol	vol	NOUN
ap-296	180	19	.	.	PUNCT
ap-296	181	1	41	41	NUM
ap-296	181	2	no	no	NOUN
ap-296	181	3	.	.	PUNCT
ap-296	182	1	6/2001	6/2001	NUM
ap-296	182	2	0.5	0.5	NUM
ap-296	182	3	0.6	0.6	NUM
ap-296	182	4	0.7	0.7	NUM
ap-296	182	5	0.8	0.8	NUM
ap-296	182	6	0.9	0.9	NUM
ap-296	182	7	1	1	NUM
ap-296	182	8	1.1	1.1	NUM
ap-296	182	9	1.2	1.2	NUM
ap-296	182	10	1.3	1.3	NUM
ap-296	182	11	0	0	NUM
ap-296	182	12	0.1	0.1	NUM
ap-296	182	13	0.2	0.2	NUM
ap-296	182	14	0.3	0.3	NUM
ap-296	182	15	0.4	0.4	NUM
ap-296	182	16	0.5	0.5	NUM
ap-296	182	17	0.6	0.6	NUM
ap-296	182	18	0.7	0.7	NUM
ap-296	182	19	0.8	0.8	NUM
ap-296	182	20	0.9	0.9	NUM
ap-296	182	21	1	1	NUM
ap-296	182	22	0.5	0.5	NUM
ap-296	182	23	0.6	0.6	NUM
ap-296	182	24	0.7	0.7	NUM
ap-296	182	25	0.8	0.8	NUM
ap-296	182	26	0.9	0.9	NUM
ap-296	182	27	1	1	NUM
ap-296	182	28	1.1	1.1	NUM
ap-296	182	29	1.2	1.2	NUM
ap-296	182	30	1.3	1.3	NUM
ap-296	182	31	0	0	NUM
ap-296	182	32	0.1	0.1	NUM
ap-296	182	33	0.2	0.2	NUM
ap-296	182	34	0.3	0.3	NUM
ap-296	182	35	0.4	0.4	NUM
ap-296	182	36	0.5	0.5	NUM
ap-296	182	37	0.6	0.6	NUM
ap-296	182	38	0.7	0.7	NUM
ap-296	182	39	0.8	0.8	NUM
ap-296	182	40	0.9	0.9	NUM
ap-296	182	41	1	1	NUM
ap-296	182	42	0.5	0.5	NUM
ap-296	182	43	0.6	0.6	NUM
ap-296	182	44	0.7	0.7	NUM
ap-296	182	45	0.8	0.8	NUM
ap-296	182	46	0.9	0.9	NUM
ap-296	182	47	1	1	NUM
ap-296	182	48	1.1	1.1	NUM
ap-296	182	49	1.2	1.2	NUM
ap-296	182	50	1.3	1.3	NUM
ap-296	182	51	0	0	NUM
ap-296	182	52	0.1	0.1	NUM
ap-296	182	53	0.2	0.2	NUM
ap-296	182	54	0.3	0.3	NUM
ap-296	182	55	0.4	0.4	NUM
ap-296	182	56	0.5	0.5	NUM
ap-296	182	57	0.6	0.6	NUM
ap-296	182	58	0.7	0.7	NUM
ap-296	182	59	0.8	0.8	NUM
ap-296	182	60	0.9	0.9	NUM
ap-296	182	61	1	1	NUM
ap-296	182	62	0.5	0.5	NUM
ap-296	182	63	0.6	0.6	NUM
ap-296	182	64	0.7	0.7	NUM
ap-296	182	65	0.8	0.8	NUM
ap-296	182	66	0.9	0.9	NUM
ap-296	182	67	1	1	NUM
ap-296	182	68	1.1	1.1	NUM
ap-296	182	69	1.2	1.2	NUM
ap-296	182	70	1.3	1.3	NUM
ap-296	182	71	0	0	NUM
ap-296	182	72	0.1	0.1	NUM
ap-296	182	73	0.2	0.2	NUM
ap-296	182	74	0.3	0.3	NUM
ap-296	182	75	0.4	0.4	NUM
ap-296	182	76	0.5	0.5	NUM
ap-296	182	77	0.6	0.6	NUM
ap-296	182	78	0.7	0.7	NUM
ap-296	182	79	0.8	0.8	NUM
ap-296	182	80	0.9	0.9	NUM
ap-296	182	81	1	1	NUM
ap-296	182	82	a	a	DET
ap-296	182	83	)	)	PUNCT
ap-296	182	84	b	b	NOUN
ap-296	182	85	)	)	PUNCT
ap-296	182	86	c	c	NOUN
ap-296	182	87	)	)	PUNCT
ap-296	183	1	d	d	NOUN
ap-296	183	2	)	)	PUNCT
ap-296	183	3	x	x	SYM
ap-296	183	4	t	t	PROPN
ap-296	183	5	(	(	PUNCT
ap-296	183	6	18	18	NUM
ap-296	183	7	)	)	PUNCT
ap-296	183	8	�	�	PROPN
ap-296	183	9	x	x	SYM
ap-296	183	10	t	t	PROPN
ap-296	183	11	(	(	PUNCT
ap-296	183	12	)	)	PUNCT
ap-296	183	13	�	�	PROPN
ap-296	183	14	�	�	PROPN
ap-296	183	15	x	x	SYM
ap-296	183	16	t	t	PROPN
ap-296	183	17	(	(	PUNCT
ap-296	183	18	12	12	NUM
ap-296	183	19	)	)	PUNCT
ap-296	183	20	�	�	PROPN
ap-296	183	21	x	x	SYM
ap-296	183	22	t	t	PROPN
ap-296	183	23	(	(	PUNCT
ap-296	183	24	)	)	PUNCT
ap-296	183	25	fig	fig	NOUN
ap-296	183	26	.	.	PUNCT
ap-296	184	1	7	7	NUM
ap-296	184	2	:	:	PUNCT
ap-296	184	3	membership	membership	NOUN
ap-296	184	4	functions	function	NOUN
ap-296	184	5	of	of	ADP
ap-296	184	6	reference	reference	NOUN
ap-296	184	7	fuzzy	fuzzy	ADJ
ap-296	184	8	sets	set	NOUN
ap-296	184	9	for	for	ADP
ap-296	184	10	inputs	input	NOUN
ap-296	184	11	a	a	X
ap-296	184	12	)	)	PUNCT
ap-296	184	13	x(t	x(t	PROPN
ap-296	184	14	�	�	NOUN
ap-296	184	15	18	18	NUM
ap-296	184	16	)	)	PUNCT
ap-296	184	17	,	,	PUNCT
ap-296	184	18	b	b	X
ap-296	184	19	)	)	PUNCT
ap-296	184	20	x	x	SYM
ap-296	184	21	(	(	PUNCT
ap-296	184	22	t	t	PROPN
ap-296	184	23	�	�	PROPN
ap-296	184	24	12	12	NUM
ap-296	184	25	)	)	PUNCT
ap-296	184	26	,	,	PUNCT
ap-296	184	27	c	c	X
ap-296	184	28	)	)	PUNCT
ap-296	184	29	x	x	SYM
ap-296	184	30	(	(	PUNCT
ap-296	184	31	t	t	PROPN
ap-296	184	32	�	�	PROPN
ap-296	184	33	6	6	NUM
ap-296	184	34	)	)	PUNCT
ap-296	184	35	,	,	PUNCT
ap-296	184	36	and	and	CCONJ
ap-296	184	37	d	d	X
ap-296	184	38	)	)	PUNCT
ap-296	185	1	x	x	SYM
ap-296	185	2	(	(	PUNCT
ap-296	185	3	t	t	NOUN
ap-296	185	4	)	)	PUNCT
ap-296	185	5	(	(	PUNCT
ap-296	185	6	dashed	dash	VERB
ap-296	185	7	line	line	NOUN
ap-296	185	8	:	:	PUNCT
ap-296	185	9	normalized	normalize	VERB
ap-296	185	10	mfs	mfs	NOUN
ap-296	185	11	before	before	ADP
ap-296	185	12	learning	learn	VERB
ap-296	185	13	.	.	PUNCT
ap-296	186	1	solid	solid	ADJ
ap-296	186	2	line	line	NOUN
ap-296	186	3	:	:	PUNCT
ap-296	186	4	optimized	optimize	VERB
ap-296	186	5	mfs	mfs	NOUN
ap-296	186	6	after	after	ADP
ap-296	186	7	using	use	VERB
ap-296	186	8	laga	laga	NOUN
ap-296	186	9	)	)	PUNCT
ap-296	186	10	2900	2900	NUM
ap-296	186	11	2910	2910	NUM
ap-296	186	12	2920	2920	NUM
ap-296	186	13	2930	2930	NUM
ap-296	186	14	2940	2940	NUM
ap-296	186	15	2950	2950	NUM
ap-296	186	16	2960	2960	NUM
ap-296	186	17	2970	2970	NUM
ap-296	186	18	2980	2980	NUM
ap-296	186	19	2990	2990	NUM
ap-296	186	20	0.5	0.5	NUM
ap-296	186	21	0.6	0.6	NUM
ap-296	186	22	0.7	0.7	NUM
ap-296	186	23	0.8	0.8	NUM
ap-296	186	24	0.9	0.9	NUM
ap-296	186	25	1	1	NUM
ap-296	186	26	1.1	1.1	NUM
ap-296	186	27	1.2	1.2	NUM
ap-296	186	28	1.3	1.3	NUM
ap-296	186	29	actual	actual	ADJ
ap-296	186	30	output	output	NOUN
ap-296	186	31	model	model	NOUN
ap-296	186	32	output	output	NOUN
ap-296	186	33	time	time	NOUN
ap-296	186	34	sample	sample	NOUN
ap-296	186	35	o	o	X
ap-296	186	36	u	u	NOUN
ap-296	186	37	tp	tp	ADP
ap-296	186	38	u	u	PROPN
ap-296	186	39	t	t	PROPN
ap-296	186	40	fig	fig	NOUN
ap-296	186	41	.	.	PUNCT
ap-296	187	1	8	8	NUM
ap-296	187	2	:	:	PUNCT
ap-296	187	3	comparing	compare	VERB
ap-296	187	4	the	the	DET
ap-296	187	5	actual	actual	ADJ
ap-296	187	6	process	process	NOUN
ap-296	187	7	with	with	ADP
ap-296	187	8	fuzzy	fuzzy	ADJ
ap-296	187	9	model	model	NOUN
ap-296	187	10	after	after	ADP
ap-296	187	11	training	train	VERB
ap-296	187	12	fig	fig	NOUN
ap-296	187	13	.	.	PUNCT
ap-296	188	1	10	10	NUM
ap-296	188	2	:	:	PUNCT
ap-296	188	3	membership	membership	NOUN
ap-296	188	4	functions	function	NOUN
ap-296	188	5	of	of	ADP
ap-296	188	6	reference	reference	NOUN
ap-296	188	7	fuzzy	fuzzy	ADJ
ap-296	188	8	sets	set	NOUN
ap-296	188	9	for	for	ADP
ap-296	188	10	inputs	input	NOUN
ap-296	188	11	x	x	SYM
ap-296	188	12	(	(	PUNCT
ap-296	188	13	t	t	PROPN
ap-296	188	14	�	�	PROPN
ap-296	188	15	18	18	NUM
ap-296	188	16	)	)	PUNCT
ap-296	188	17	.	.	PUNCT
ap-296	189	1	(	(	PUNCT
ap-296	189	2	dashed	dash	VERB
ap-296	189	3	line	line	NOUN
ap-296	189	4	:	:	PUNCT
ap-296	189	5	normalized	normalize	VERB
ap-296	189	6	mfs	mfs	NOUN
ap-296	189	7	before	before	ADP
ap-296	189	8	learning	learn	VERB
ap-296	189	9	.	.	PUNCT
ap-296	190	1	solid	solid	ADJ
ap-296	190	2	line	line	NOUN
ap-296	190	3	:	:	PUNCT
ap-296	190	4	optimized	optimize	VERB
ap-296	190	5	mfs	mfs	NOUN
ap-296	190	6	after	after	ADP
ap-296	190	7	using	use	VERB
ap-296	190	8	laga	laga	NOUN
ap-296	190	9	)	)	PUNCT
ap-296	190	10	0	0	NUM
ap-296	191	1	50	50	NUM
ap-296	191	2	100	100	NUM
ap-296	191	3	150	150	NUM
ap-296	191	4	200	200	NUM
ap-296	191	5	250	250	NUM
ap-296	191	6	300	300	NUM
ap-296	191	7	350	350	NUM
ap-296	191	8	400	400	NUM
ap-296	191	9	450	450	NUM
ap-296	191	10	0.1	0.1	NUM
ap-296	191	11	0.2	0.2	NUM
ap-296	191	12	0.3	0.3	NUM
ap-296	191	13	0.4	0.4	NUM
ap-296	191	14	0.5	0.5	NUM
ap-296	191	15	0.6	0.6	NUM
ap-296	191	16	0.7	0.7	NUM
ap-296	191	17	0.8	0.8	NUM
ap-296	191	18	generations	generation	NOUN
ap-296	192	1	r	r	NOUN
ap-296	192	2	m	m	NOUN
ap-296	192	3	s	s	NOUN
ap-296	192	4	e	e	NOUN
ap-296	192	5	rmse	rmse	NOUN
ap-296	192	6	at	at	ADP
ap-296	192	7	pop	pop	NOUN
ap-296	192	8	-	-	PUNCT
ap-296	192	9	size	size	NOUN
ap-296	192	10	=	=	NOUN
ap-296	192	11	200	200	NUM
ap-296	192	12	fig	fig	NOUN
ap-296	192	13	.	.	PUNCT
ap-296	193	1	9	9	NUM
ap-296	193	2	:	:	PUNCT
ap-296	193	3	laga	laga	NOUN
ap-296	193	4	convergence	convergence	NOUN
ap-296	193	5	rate	rate	NOUN
ap-296	193	6	at	at	ADP
ap-296	193	7	population	population	NOUN
ap-296	193	8	size	size	NOUN
ap-296	193	9	200	200	NUM
ap-296	193	10	6.2	6.2	NUM
ap-296	193	11	nonlinear	nonlinear	ADJ
ap-296	193	12	discrete	discrete	ADJ
ap-296	193	13	time	time	NOUN
ap-296	193	14	process	process	NOUN
ap-296	193	15	modeling	modeling	NOUN
ap-296	193	16	and	and	CCONJ
ap-296	193	17	identification	identification	NOUN
ap-296	193	18	this	this	DET
ap-296	193	19	example	example	NOUN
ap-296	193	20	is	be	AUX
ap-296	193	21	taken	take	VERB
ap-296	193	22	from	from	ADP
ap-296	193	23	[	[	X
ap-296	193	24	12	12	NUM
ap-296	193	25	]	]	PUNCT
ap-296	193	26	,	,	PUNCT
ap-296	193	27	[	[	X
ap-296	193	28	15	15	NUM
ap-296	193	29	]	]	X
ap-296	193	30	,	,	PUNCT
ap-296	193	31	in	in	ADP
ap-296	193	32	which	which	PRON
ap-296	193	33	the	the	DET
ap-296	193	34	plant	plant	NOUN
ap-296	193	35	to	to	PART
ap-296	193	36	be	be	AUX
ap-296	193	37	identified	identify	VERB
ap-296	193	38	is	be	AUX
ap-296	193	39	governed	govern	VERB
ap-296	193	40	by	by	ADP
ap-296	193	41	the	the	DET
ap-296	193	42	differential	differential	ADJ
ap-296	193	43	equation	equation	NOUN
ap-296	193	44	(	(	PUNCT
ap-296	193	45	19	19	NUM
ap-296	193	46	):	):	PUNCT
ap-296	193	47	�	�	PROPN
ap-296	193	48	�	�	PROPN
ap-296	193	49	�	�	PROPN
ap-296	193	50	�	�	PROPN
ap-296	193	51	�	�	PROPN
ap-296	193	52	�	�	PROPN
ap-296	193	53	�	�	PROPN
ap-296	193	54	�	�	PROPN
ap-296	193	55	�	�	PROPN
ap-296	193	56	�	�	PROPN
ap-296	194	1	y	y	PROPN
ap-296	194	2	k	k	PROPN
ap-296	194	3	k	k	PROPN
ap-296	195	1	k	k	PROPN
ap-296	195	2	g	g	PROPN
ap-296	195	3	u	u	PROPN
ap-296	195	4	k	k	PROPN
ap-296	195	5	�	�	PROPN
ap-296	195	6	�	�	PROPN
ap-296	195	7	�	�	PROPN
ap-296	195	8	�	�	PROPN
ap-296	195	9	�	�	PROPN
ap-296	195	10	1	1	NUM
ap-296	195	11	03	03	NUM
ap-296	195	12	06	06	NUM
ap-296	195	13	1	1	NUM
ap-296	195	14	.	.	PUNCT
ap-296	195	15	.	.	PUNCT
ap-296	196	1	(	(	PUNCT
ap-296	196	2	19	19	NUM
ap-296	196	3	)	)	PUNCT
ap-296	196	4	where	where	SCONJ
ap-296	196	5	the	the	DET
ap-296	196	6	unknown	unknown	ADJ
ap-296	196	7	function	function	NOUN
ap-296	196	8	has	have	VERB
ap-296	196	9	the	the	DET
ap-296	196	10	form	form	NOUN
ap-296	196	11	�	�	PROPN
ap-296	196	12	�	�	PROPN
ap-296	196	13	�	�	PROPN
ap-296	196	14	�	�	PROPN
ap-296	196	15	�	�	PROPN
ap-296	196	16	�	�	PROPN
ap-296	196	17	�	�	PROPN
ap-296	196	18	�	�	PROPN
ap-296	196	19	g	g	NOUN
ap-296	196	20	u	u	X
ap-296	197	1	u	u	X
ap-296	197	2	u	u	X
ap-296	197	3	u	u	PRON
ap-296	197	4	�	�	PROPN
ap-296	197	5	�	�	PROPN
ap-296	197	6	�	�	PROPN
ap-296	197	7	06	06	NUM
ap-296	197	8	03	03	NUM
ap-296	197	9	3	3	NUM
ap-296	197	10	01	01	NUM
ap-296	197	11	5	5	NUM
ap-296	197	12	.	.	PUNCT
ap-296	197	13	sin	sin	NOUN
ap-296	197	14	.	.	PUNCT
ap-296	198	1	sin	sin	NOUN
ap-296	198	2	.	.	PUNCT
ap-296	199	1	sin	sin	PROPN
ap-296	199	2	�	�	PROPN
ap-296	199	3	�	�	PROPN
ap-296	199	4	�	�	PROPN
ap-296	199	5	.	.	PUNCT
ap-296	200	1	the	the	DET
ap-296	200	2	plant	plant	NOUN
ap-296	200	3	is	be	AUX
ap-296	200	4	modeled	model	VERB
ap-296	200	5	using	use	VERB
ap-296	200	6	flnn	flnn	NOUN
ap-296	200	7	,	,	PUNCT
ap-296	200	8	as	as	SCONJ
ap-296	200	9	described	describe	VERB
ap-296	200	10	in	in	ADP
ap-296	200	11	section	section	NOUN
ap-296	200	12	2	2	NUM
ap-296	200	13	.	.	PUNCT
ap-296	201	1	the	the	DET
ap-296	201	2	model	model	NOUN
ap-296	201	3	has	have	VERB
ap-296	201	4	three	three	NUM
ap-296	201	5	input	input	NOUN
ap-296	201	6	variables	variable	NOUN
ap-296	201	7	u(k	u(k	PROPN
ap-296	201	8	)	)	PUNCT
ap-296	201	9	,	,	PUNCT
ap-296	201	10	y(k	y(k	PROPN
ap-296	201	11	)	)	PUNCT
ap-296	201	12	,	,	PUNCT
ap-296	201	13	and	and	CCONJ
ap-296	201	14	y(k	y(k	PROPN
ap-296	201	15	�	�	PROPN
ap-296	201	16	1	1	NUM
ap-296	201	17	)	)	PUNCT
ap-296	201	18	and	and	CCONJ
ap-296	201	19	a	a	DET
ap-296	201	20	single	single	ADJ
ap-296	201	21	output	output	NOUN
ap-296	201	22	y(k+1	y(k+1	NOUN
ap-296	201	23	)	)	PUNCT
ap-296	201	24	,	,	PUNCT
ap-296	201	25	treated	treat	VERB
ap-296	201	26	as	as	ADP
ap-296	201	27	linguistic	linguistic	ADJ
ap-296	201	28	variables	variable	NOUN
ap-296	201	29	.	.	PUNCT
ap-296	202	1	the	the	DET
ap-296	202	2	universe	universe	NOUN
ap-296	202	3	of	of	ADP
ap-296	202	4	discourse	discourse	NOUN
ap-296	202	5	of	of	ADP
ap-296	202	6	every	every	DET
ap-296	202	7	variable	variable	NOUN
ap-296	202	8	is	be	AUX
ap-296	202	9	partitioned	partition	VERB
ap-296	202	10	into	into	ADP
ap-296	202	11	five	five	NUM
ap-296	202	12	fuzzy	fuzzy	ADJ
ap-296	202	13	sets	set	NOUN
ap-296	202	14	with	with	ADP
ap-296	202	15	symmetrical	symmetrical	ADJ
ap-296	202	16	gaussian	gaussian	ADJ
ap-296	202	17	membership	membership	NOUN
ap-296	202	18	functions	function	NOUN
ap-296	202	19	.	.	PUNCT
ap-296	203	1	there	there	PRON
ap-296	203	2	are	be	VERB
ap-296	203	3	30	30	NUM
ap-296	203	4	parameters	parameter	NOUN
ap-296	203	5	at	at	ADP
ap-296	203	6	the	the	DET
ap-296	203	7	input	input	NOUN
ap-296	203	8	of	of	ADP
ap-296	203	9	the	the	DET
ap-296	203	10	flnn	flnn	NOUN
ap-296	203	11	model	model	NOUN
ap-296	203	12	.	.	PUNCT
ap-296	204	1	using	use	VERB
ap-296	204	2	the	the	DET
ap-296	204	3	wang	wang	PROPN
ap-296	204	4	technique	technique	NOUN
ap-296	204	5	for	for	ADP
ap-296	204	6	generating	generate	VERB
ap-296	204	7	rules	rule	NOUN
ap-296	204	8	from	from	ADP
ap-296	204	9	the	the	DET
ap-296	204	10	given	give	VERB
ap-296	204	11	data	datum	NOUN
ap-296	204	12	,	,	PUNCT
ap-296	204	13	we	we	PRON
ap-296	204	14	have	have	VERB
ap-296	204	15	20	20	NUM
ap-296	204	16	rules	rule	NOUN
ap-296	204	17	.	.	PUNCT
ap-296	205	1	this	this	PRON
ap-296	205	2	means	mean	VERB
ap-296	205	3	we	we	PRON
ap-296	205	4	have	have	VERB
ap-296	205	5	20	20	NUM
ap-296	205	6	weights	weight	NOUN
ap-296	205	7	,	,	PUNCT
ap-296	205	8	and	and	CCONJ
ap-296	205	9	20	20	NUM
ap-296	205	10	centroids	centroid	NOUN
ap-296	205	11	represented	represent	VERB
ap-296	205	12	by	by	ADP
ap-296	205	13	singletons	singleton	NOUN
ap-296	205	14	.	.	PUNCT
ap-296	206	1	thus	thus	ADV
ap-296	206	2	a	a	DET
ap-296	206	3	total	total	NOUN
ap-296	206	4	of	of	ADP
ap-296	206	5	70	70	NUM
ap-296	206	6	parameters	parameter	NOUN
ap-296	206	7	(	(	PUNCT
ap-296	206	8	2	2	NUM
ap-296	206	9	5membership_functions	5membership_function	NOUN
ap-296	206	10	3variables	3variables	NUM
ap-296	206	11	+	+	NUM
ap-296	206	12	20weights	20weight	NOUN
ap-296	206	13	+	+	CCONJ
ap-296	206	14	20centroids	20centroid	NOUN
ap-296	206	15	)	)	PUNCT
ap-296	206	16	need	need	VERB
ap-296	206	17	to	to	PART
ap-296	206	18	be	be	AUX
ap-296	206	19	optimized	optimize	VERB
ap-296	206	20	using	use	VERB
ap-296	206	21	laga	laga	NOUN
ap-296	206	22	.	.	PUNCT
ap-296	207	1	the	the	DET
ap-296	207	2	learning	learn	VERB
ap-296	207	3	procedure	procedure	NOUN
ap-296	207	4	of	of	ADP
ap-296	207	5	laga	laga	NOUN
ap-296	207	6	is	be	AUX
ap-296	207	7	applied	apply	VERB
ap-296	207	8	as	as	ADP
ap-296	207	9	in	in	ADP
ap-296	207	10	the	the	DET
ap-296	207	11	first	first	ADJ
ap-296	207	12	numerical	numerical	PROPN
ap-296	207	13	example	example	NOUN
ap-296	207	14	.	.	PUNCT
ap-296	208	1	fig	fig	NOUN
ap-296	208	2	.	.	PUNCT
ap-296	209	1	5	5	NUM
ap-296	209	2	shows	show	VERB
ap-296	209	3	the	the	DET
ap-296	209	4	block	block	NOUN
ap-296	209	5	diagram	diagram	NOUN
ap-296	209	6	for	for	ADP
ap-296	209	7	the	the	DET
ap-296	209	8	laga	laga	NOUN
ap-296	209	9	optimization	optimization	NOUN
ap-296	209	10	process	process	NOUN
ap-296	209	11	for	for	ADP
ap-296	209	12	optimizing	optimize	VERB
ap-296	209	13	the	the	DET
ap-296	209	14	flnn	flnn	NOUN
ap-296	209	15	model	model	NOUN
ap-296	209	16	parameters	parameter	NOUN
ap-296	209	17	of	of	ADP
ap-296	209	18	the	the	DET
ap-296	209	19	second	second	ADJ
ap-296	209	20	numerical	numerical	ADJ
ap-296	209	21	example	example	NOUN
ap-296	209	22	.	.	PUNCT
ap-296	210	1	the	the	DET
ap-296	210	2	process	process	NOUN
ap-296	210	3	starts	start	VERB
ap-296	210	4	with	with	ADP
ap-296	210	5	zero	zero	NUM
ap-296	210	6	initial	initial	ADJ
ap-296	210	7	conditions	condition	NOUN
ap-296	210	8	.	.	PUNCT
ap-296	211	1	the	the	DET
ap-296	211	2	first	first	ADJ
ap-296	211	3	250	250	NUM
ap-296	211	4	data	datum	NOUN
ap-296	211	5	points	point	NOUN
ap-296	211	6	are	be	AUX
ap-296	211	7	used	use	VERB
ap-296	211	8	to	to	PART
ap-296	211	9	build	build	VERB
ap-296	211	10	the	the	DET
ap-296	211	11	fuzzy	fuzzy	ADJ
ap-296	211	12	model	model	NOUN
ap-296	211	13	at	at	ADP
ap-296	211	14	,	,	PUNCT
ap-296	211	15	while	while	SCONJ
ap-296	211	16	the	the	DET
ap-296	211	17	remaining	remain	VERB
ap-296	211	18	450	450	NUM
ap-296	211	19	data	datum	NOUN
ap-296	211	20	points	point	NOUN
ap-296	211	21	are	be	AUX
ap-296	211	22	used	use	VERB
ap-296	211	23	to	to	PART
ap-296	211	24	identify	identify	VERB
ap-296	211	25	an	an	DET
ap-296	211	26	flnn	flnn	NOUN
ap-296	211	27	model	model	NOUN
ap-296	211	28	.	.	PUNCT
ap-296	212	1	as	as	SCONJ
ap-296	212	2	explained	explain	VERB
ap-296	212	3	in	in	ADP
ap-296	212	4	the	the	DET
ap-296	212	5	first	first	ADJ
ap-296	212	6	application	application	NOUN
ap-296	212	7	we	we	PRON
ap-296	212	8	determined	determine	VERB
ap-296	212	9	the	the	DET
ap-296	212	10	constraints	constraint	NOUN
ap-296	212	11	for	for	ADP
ap-296	212	12	this	this	DET
ap-296	212	13	application	application	NOUN
ap-296	212	14	based	base	VERB
ap-296	212	15	on	on	ADP
ap-296	212	16	second	second	ADJ
ap-296	212	17	order	order	NOUN
ap-296	212	18	fuzzy	fuzzy	ADJ
ap-296	212	19	sets	set	NOUN
ap-296	212	20	with	with	ADP
ap-296	212	21	�	�	NOUN
ap-296	212	22	=	=	NOUN
ap-296	212	23	0.28	0.28	NUM
ap-296	212	24	.	.	PUNCT
ap-296	213	1	fig	fig	NOUN
ap-296	213	2	.	.	PUNCT
ap-296	214	1	11	11	NUM
ap-296	214	2	shows	show	VERB
ap-296	214	3	that	that	SCONJ
ap-296	214	4	the	the	DET
ap-296	214	5	flnn	flnn	NOUN
ap-296	214	6	model	model	NOUN
ap-296	214	7	has	have	VERB
ap-296	214	8	a	a	DET
ap-296	214	9	good	good	ADJ
ap-296	214	10	match	match	NOUN
ap-296	214	11	with	with	ADP
ap-296	214	12	the	the	DET
ap-296	214	13	actual	actual	ADJ
ap-296	214	14	model	model	NOUN
ap-296	214	15	,	,	PUNCT
ap-296	214	16	with	with	ADP
ap-296	214	17	an	an	DET
ap-296	214	18	mse	mse	NOUN
ap-296	214	19	of	of	ADP
ap-296	214	20	0.0473	0.0473	NUM
ap-296	214	21	,	,	PUNCT
ap-296	214	22	and	and	CCONJ
ap-296	214	23	an	an	DET
ap-296	214	24	rmse	rmse	NOUN
ap-296	214	25	of	of	ADP
ap-296	214	26	0.0607	0.0607	NUM
ap-296	214	27	,	,	PUNCT
ap-296	214	28	as	as	SCONJ
ap-296	214	29	shown	show	VERB
ap-296	214	30	in	in	ADP
ap-296	214	31	fig	fig	NOUN
ap-296	214	32	.	.	PUNCT
ap-296	215	1	12	12	NUM
ap-296	215	2	.	.	PUNCT
ap-296	215	3	fig	fig	NOUN
ap-296	215	4	.	.	PUNCT
ap-296	216	1	13	13	NUM
ap-296	216	2	depicts	depict	VERB
ap-296	216	3	the	the	DET
ap-296	216	4	membership	membership	NOUN
ap-296	216	5	functions	function	NOUN
ap-296	216	6	for	for	ADP
ap-296	216	7	each	each	DET
ap-296	216	8	input	input	NOUN
ap-296	216	9	variable	variable	NOUN
ap-296	216	10	before	before	ADV
ap-296	216	11	and	and	CCONJ
ap-296	216	12	after	after	ADP
ap-296	216	13	training	training	NOUN
ap-296	216	14	using	use	VERB
ap-296	216	15	laga	laga	NOUN
ap-296	216	16	.	.	PUNCT
ap-296	217	1	fig	fig	NOUN
ap-296	217	2	.	.	PUNCT
ap-296	218	1	14	14	NUM
ap-296	218	2	shows	show	VERB
ap-296	218	3	the	the	DET
ap-296	218	4	output	output	NOUN
ap-296	218	5	model	model	NOUN
ap-296	218	6	and	and	CCONJ
ap-296	218	7	the	the	DET
ap-296	218	8	plant	plant	NOUN
ap-296	218	9	for	for	ADP
ap-296	218	10	the	the	DET
ap-296	218	11	input	input	NOUN
ap-296	218	12	:	:	PUNCT
ap-296	218	13	�	�	PROPN
ap-296	218	14	�	�	PROPN
ap-296	218	15	�	�	PROPN
ap-296	218	16	�	�	PROPN
ap-296	218	17	u	u	PROPN
ap-296	218	18	k	k	PROPN
ap-296	218	19	k	k	PROPN
ap-296	218	20	�	�	PROPN
ap-296	218	21	�	�	PROPN
ap-296	218	22	�	�	PROPN
ap-296	218	23	�	�	PROPN
ap-296	218	24	�	�	PROPN
ap-296	218	25	05	05	NUM
ap-296	218	26	2	2	NUM
ap-296	218	27	250	250	NUM
ap-296	218	28	.	.	PUNCT
ap-296	219	1	sin	sin	PROPN
ap-296	219	2	�	�	PROPN
ap-296	219	3	for	for	ADP
ap-296	219	4	1	1	NUM
ap-296	219	5	k	k	NOUN
ap-296	219	6	250	250	NUM
ap-296	219	7	and	and	CCONJ
ap-296	219	8	501	501	NUM
ap-296	219	9	k	k	NOUN
ap-296	219	10	700	700	NUM
ap-296	219	11	,	,	PUNCT
ap-296	219	12	and	and	CCONJ
ap-296	219	13	�	�	PROPN
ap-296	219	14	�	�	PROPN
ap-296	219	15	�	�	PROPN
ap-296	219	16	�	�	PROPN
ap-296	219	17	�	�	PROPN
ap-296	219	18	�	�	PROPN
ap-296	219	19	u	u	PROPN
ap-296	219	20	k	k	PROPN
ap-296	219	21	k	k	PROPN
ap-296	219	22	k	k	PROPN
ap-296	219	23	�	�	PROPN
ap-296	219	24	�	�	PROPN
ap-296	219	25	�	�	PROPN
ap-296	219	26	�	�	PROPN
ap-296	219	27	05	05	NUM
ap-296	219	28	2	2	NUM
ap-296	219	29	250	250	NUM
ap-296	219	30	05	05	NUM
ap-296	219	31	2	2	NUM
ap-296	219	32	25	25	NUM
ap-296	219	33	25	25	NUM
ap-296	219	34	.	.	PUNCT
ap-296	220	1	sin	sin	NOUN
ap-296	220	2	.	.	PUNCT
ap-296	221	1	sin	sin	PROPN
ap-296	221	2	�	�	PROPN
ap-296	221	3	�	�	PROPN
ap-296	221	4	for	for	ADP
ap-296	221	5	1	1	NUM
ap-296	221	6	k	k	PROPN
ap-296	221	7	500	500	NUM
ap-296	221	8	.	.	PUNCT
ap-296	222	1	©	©	PROPN
ap-296	222	2	czech	czech	PROPN
ap-296	222	3	technical	technical	PROPN
ap-296	222	4	university	university	PROPN
ap-296	222	5	publishing	publishing	NOUN
ap-296	222	6	house	house	NOUN
ap-296	222	7	http://ctn.cvut.cz/ap/	http://ctn.cvut.cz/ap/	PROPN
ap-296	222	8	75	75	NUM
ap-296	222	9	acta	acta	PROPN
ap-296	222	10	polytechnica	polytechnica	PROPN
ap-296	222	11	vol	vol	NOUN
ap-296	222	12	.	.	PUNCT
ap-296	223	1	41	41	NUM
ap-296	223	2	no	no	NOUN
ap-296	223	3	.	.	PUNCT
ap-296	224	1	6/2001	6/2001	NUM
ap-296	224	2	0	0	NUM
ap-296	224	3	100	100	NUM
ap-296	224	4	200	200	NUM
ap-296	224	5	300	300	NUM
ap-296	224	6	400	400	NUM
ap-296	224	7	500	500	NUM
ap-296	224	8	600	600	NUM
ap-296	224	9	700	700	NUM
ap-296	225	1	-6	-6	ADP
ap-296	225	2	-4	-4	INTJ
ap-296	226	1	-2	-2	NOUN
ap-296	226	2	0	0	NUM
ap-296	226	3	2	2	NUM
ap-296	226	4	4	4	NUM
ap-296	226	5	6	6	NUM
ap-296	226	6	time	time	NOUN
ap-296	226	7	sample	sample	NOUN
ap-296	226	8	fig	fig	NOUN
ap-296	226	9	.	.	PUNCT
ap-296	227	1	11	11	NUM
ap-296	227	2	:	:	PUNCT
ap-296	227	3	outputs	output	NOUN
ap-296	227	4	of	of	ADP
ap-296	227	5	the	the	DET
ap-296	227	6	plant	plant	NOUN
ap-296	227	7	(	(	PUNCT
ap-296	227	8	solid	solid	ADJ
ap-296	227	9	line	line	NOUN
ap-296	227	10	)	)	PUNCT
ap-296	227	11	,	,	PUNCT
ap-296	227	12	and	and	CCONJ
ap-296	227	13	the	the	DET
ap-296	227	14	flnn	flnn	NOUN
ap-296	227	15	model	model	NOUN
ap-296	227	16	(	(	PUNCT
ap-296	227	17	dashed	dash	VERB
ap-296	227	18	line	line	NOUN
ap-296	227	19	)	)	PUNCT
ap-296	227	20	for	for	ADP
ap-296	227	21	u	u	PROPN
ap-296	227	22	(	(	PUNCT
ap-296	227	23	k	k	NOUN
ap-296	227	24	)	)	PUNCT
ap-296	227	25	�	�	PROPN
ap-296	227	26	sin(2	sin(2	PROPN
ap-296	227	27	�	�	PROPN
ap-296	227	28	k/250	k/250	PROPN
ap-296	227	29	)	)	PUNCT
ap-296	227	30	50	50	NUM
ap-296	227	31	100	100	NUM
ap-296	227	32	150	150	NUM
ap-296	227	33	200	200	NUM
ap-296	227	34	250	250	NUM
ap-296	227	35	300	300	NUM
ap-296	227	36	350	350	NUM
ap-296	227	37	400	400	NUM
ap-296	227	38	450	450	NUM
ap-296	227	39	500	500	NUM
ap-296	227	40	0.06	0.06	NUM
ap-296	227	41	0.065	0.065	NUM
ap-296	227	42	0.07	0.07	NUM
ap-296	227	43	0.075	0.075	NUM
ap-296	227	44	0.08	0.08	NUM
ap-296	227	45	0.085	0.085	NUM
ap-296	227	46	0.09	0.09	NUM
ap-296	227	47	0.095	0.095	NUM
ap-296	227	48	r	r	NOUN
ap-296	227	49	m	m	NOUN
ap-296	227	50	s	s	PROPN
ap-296	227	51	e	e	NOUN
ap-296	227	52	fig	fig	NOUN
ap-296	227	53	.	.	PUNCT
ap-296	228	1	12	12	NUM
ap-296	228	2	:	:	PUNCT
ap-296	228	3	laga	laga	NOUN
ap-296	228	4	convergence	convergence	NOUN
ap-296	228	5	rate	rate	NOUN
ap-296	228	6	at	at	ADP
ap-296	228	7	population	population	NOUN
ap-296	228	8	size	size	NOUN
ap-296	228	9	200	200	NUM
ap-296	228	10	-4	-4	INTJ
ap-296	228	11	-3	-3	INTJ
ap-296	229	1	-2	-2	INTJ
ap-296	229	2	-1	-1	SYM
ap-296	229	3	0	0	NUM
ap-296	229	4	1	1	NUM
ap-296	229	5	2	2	NUM
ap-296	229	6	3	3	NUM
ap-296	229	7	4	4	NUM
ap-296	229	8	5	5	NUM
ap-296	229	9	0	0	NUM
ap-296	229	10	0.1	0.1	NUM
ap-296	229	11	0.2	0.2	NUM
ap-296	229	12	0.3	0.3	NUM
ap-296	229	13	0.4	0.4	NUM
ap-296	229	14	0.5	0.5	NUM
ap-296	229	15	0.6	0.6	NUM
ap-296	229	16	0.7	0.7	NUM
ap-296	229	17	0.8	0.8	NUM
ap-296	229	18	0.9	0.9	NUM
ap-296	229	19	1	1	NUM
ap-296	229	20	-4	-4	PRON
ap-296	229	21	-3	-3	INTJ
ap-296	230	1	-2	-2	INTJ
ap-296	231	1	-1	-1	SYM
ap-296	231	2	0	0	NUM
ap-296	232	1	1	1	NUM
ap-296	232	2	2	2	NUM
ap-296	232	3	3	3	NUM
ap-296	232	4	4	4	NUM
ap-296	232	5	5	5	NUM
ap-296	232	6	0	0	NUM
ap-296	232	7	0.1	0.1	NUM
ap-296	232	8	0.2	0.2	NUM
ap-296	232	9	0.3	0.3	NUM
ap-296	232	10	0.4	0.4	NUM
ap-296	232	11	0.5	0.5	NUM
ap-296	232	12	0.6	0.6	NUM
ap-296	232	13	0.7	0.7	NUM
ap-296	232	14	0.8	0.8	NUM
ap-296	232	15	0.9	0.9	NUM
ap-296	232	16	1	1	NUM
ap-296	232	17	y	y	PROPN
ap-296	232	18	k	k	PROPN
ap-296	232	19	(	(	PUNCT
ap-296	232	20	)	)	PUNCT
ap-296	232	21	y	y	PROPN
ap-296	232	22	k	k	PROPN
ap-296	232	23	(	(	PUNCT
ap-296	232	24	1	1	NUM
ap-296	232	25	)	)	PUNCT
ap-296	232	26	�	�	PROPN
ap-296	232	27	�	�	PROPN
ap-296	232	28	�	�	PROPN
ap-296	232	29	�	�	PROPN
ap-296	232	30	-0.8	-0.8	PROPN
ap-296	232	31	-0.6	-0.6	PROPN
ap-296	232	32	-0.4	-0.4	X
ap-296	232	33	-0.2	-0.2	PROPN
ap-296	232	34	0	0	NUM
ap-296	232	35	0.2	0.2	NUM
ap-296	232	36	0.4	0.4	NUM
ap-296	232	37	0.6	0.6	NUM
ap-296	233	1	0.8	0.8	NUM
ap-296	233	2	0	0	NUM
ap-296	233	3	0.1	0.1	NUM
ap-296	233	4	0.2	0.2	NUM
ap-296	233	5	0.3	0.3	NUM
ap-296	233	6	0.4	0.4	NUM
ap-296	233	7	0.5	0.5	NUM
ap-296	233	8	0.6	0.6	NUM
ap-296	233	9	0.7	0.7	NUM
ap-296	233	10	0.8	0.8	NUM
ap-296	233	11	0.9	0.9	NUM
ap-296	233	12	1	1	NUM
ap-296	233	13	u	u	NOUN
ap-296	233	14	k	k	NOUN
ap-296	233	15	(	(	PUNCT
ap-296	233	16	)	)	PUNCT
ap-296	233	17	(	(	PUNCT
ap-296	233	18	a	a	X
ap-296	233	19	)	)	PUNCT
ap-296	233	20	(	(	PUNCT
ap-296	233	21	b	b	X
ap-296	233	22	)	)	PUNCT
ap-296	233	23	(	(	PUNCT
ap-296	233	24	c	c	X
ap-296	233	25	)	)	PUNCT
ap-296	233	26	fig	fig	NOUN
ap-296	233	27	.	.	PUNCT
ap-296	234	1	13	13	NUM
ap-296	234	2	:	:	PUNCT
ap-296	234	3	(	(	PUNCT
ap-296	234	4	a	a	PRON
ap-296	234	5	,	,	PUNCT
ap-296	234	6	b	b	NOUN
ap-296	234	7	,	,	PUNCT
ap-296	234	8	and	and	CCONJ
ap-296	234	9	c	c	X
ap-296	234	10	)	)	PUNCT
ap-296	234	11	are	be	AUX
ap-296	234	12	input	input	ADJ
ap-296	234	13	mfs	mfs	PROPN
ap-296	234	14	:	:	PUNCT
ap-296	234	15	dashed	dash	VERB
ap-296	234	16	line	line	NOUN
ap-296	234	17	:	:	PUNCT
ap-296	234	18	normalized	normalize	VERB
ap-296	234	19	mfs	mfs	NOUN
ap-296	234	20	before	before	ADP
ap-296	234	21	learning	learn	VERB
ap-296	234	22	solid	solid	ADJ
ap-296	234	23	line	line	NOUN
ap-296	234	24	:	:	PUNCT
ap-296	234	25	optimized	optimize	VERB
ap-296	234	26	mfs	mfs	NOUN
ap-296	234	27	after	after	ADP
ap-296	234	28	using	use	VERB
ap-296	234	29	laga	laga	NOUN
ap-296	234	30	7	7	NUM
ap-296	234	31	conclusion	conclusion	NOUN
ap-296	234	32	the	the	DET
ap-296	234	33	paper	paper	NOUN
ap-296	234	34	deals	deal	NOUN
ap-296	234	35	with	with	ADP
ap-296	234	36	modeling	modeling	NOUN
ap-296	234	37	of	of	ADP
ap-296	234	38	nonlinear	nonlinear	ADJ
ap-296	234	39	systems	system	NOUN
ap-296	234	40	and	and	CCONJ
ap-296	234	41	processes	process	NOUN
ap-296	234	42	using	use	VERB
ap-296	234	43	fuzzy	fuzzy	ADJ
ap-296	234	44	logic	logic	NOUN
ap-296	234	45	neural	neural	ADJ
ap-296	234	46	networks	network	NOUN
ap-296	234	47	.	.	PUNCT
ap-296	235	1	reference	reference	NOUN
ap-296	235	2	data	datum	NOUN
ap-296	235	3	driven	drive	VERB
ap-296	235	4	identification	identification	NOUN
ap-296	235	5	of	of	ADP
ap-296	235	6	parameters	parameter	NOUN
ap-296	235	7	of	of	ADP
ap-296	235	8	fuzzy	fuzzy	ADJ
ap-296	235	9	logic	logic	NOUN
ap-296	235	10	neural	neural	ADJ
ap-296	235	11	networks	network	NOUN
ap-296	235	12	utilizing	utilize	VERB
ap-296	235	13	genetic	genetic	ADJ
ap-296	235	14	algorithms	algorithm	NOUN
ap-296	235	15	has	have	AUX
ap-296	235	16	been	be	AUX
ap-296	235	17	proposed	propose	VERB
ap-296	235	18	and	and	CCONJ
ap-296	235	19	tested	test	VERB
ap-296	235	20	.	.	PUNCT
ap-296	236	1	specification	specification	NOUN
ap-296	236	2	of	of	ADP
ap-296	236	3	parameter	parameter	NOUN
ap-296	236	4	constraints	constraint	NOUN
ap-296	236	5	related	relate	VERB
ap-296	236	6	to	to	ADP
ap-296	236	7	input	input	NOUN
ap-296	236	8	reference	reference	NOUN
ap-296	236	9	fuzzy	fuzzy	ADJ
ap-296	236	10	sets	set	NOUN
ap-296	236	11	are	be	AUX
ap-296	236	12	based	base	VERB
ap-296	236	13	on	on	ADP
ap-296	236	14	2nd	2nd	ADJ
ap-296	236	15	order	order	NOUN
ap-296	236	16	fuzzy	fuzzy	ADJ
ap-296	236	17	sets	set	NOUN
ap-296	236	18	.	.	PUNCT
ap-296	237	1	the	the	DET
ap-296	237	2	problem	problem	NOUN
ap-296	237	3	of	of	ADP
ap-296	237	4	constrained	constrain	VERB
ap-296	237	5	nonlinear	nonlinear	ADJ
ap-296	237	6	optimization	optimization	NOUN
ap-296	237	7	is	be	AUX
ap-296	237	8	solved	solve	VERB
ap-296	237	9	based	base	VERB
ap-296	237	10	on	on	ADP
ap-296	237	11	a	a	DET
ap-296	237	12	genetic	genetic	ADJ
ap-296	237	13	algorithm	algorithm	NOUN
ap-296	237	14	with	with	ADP
ap-296	237	15	variable	variable	ADJ
ap-296	237	16	crossover	crossover	NOUN
ap-296	237	17	and	and	CCONJ
ap-296	237	18	mutation	mutation	NOUN
ap-296	237	19	probabilities	probability	NOUN
ap-296	237	20	rates	rate	NOUN
ap-296	237	21	,	,	PUNCT
ap-296	237	22	laga	laga	NOUN
ap-296	238	1	[	[	X
ap-296	238	2	attia	attia	NOUN
ap-296	238	3	,	,	PUNCT
ap-296	238	4	2001	2001	NUM
ap-296	238	5	]	]	PUNCT
ap-296	238	6	.	.	PUNCT
ap-296	239	1	the	the	DET
ap-296	239	2	paper	paper	NOUN
ap-296	239	3	reports	report	VERB
ap-296	239	4	the	the	DET
ap-296	239	5	results	result	NOUN
ap-296	239	6	in	in	ADP
ap-296	239	7	dynamic	dynamic	ADJ
ap-296	239	8	process	process	NOUN
ap-296	239	9	identification	identification	NOUN
ap-296	239	10	,	,	PUNCT
ap-296	239	11	prediction	prediction	NOUN
ap-296	239	12	of	of	ADP
ap-296	239	13	time	time	NOUN
ap-296	239	14	series	series	PROPN
ap-296	239	15	in	in	ADP
ap-296	239	16	particular	particular	ADJ
ap-296	239	17	.	.	PUNCT
ap-296	240	1	the	the	DET
ap-296	240	2	performance	performance	NOUN
ap-296	240	3	of	of	ADP
ap-296	240	4	the	the	DET
ap-296	240	5	nonlinear	nonlinear	ADJ
ap-296	240	6	models	model	NOUN
ap-296	240	7	for	for	ADP
ap-296	240	8	time	time	NOUN
ap-296	240	9	series	series	PROPN
ap-296	240	10	prediction	prediction	NOUN
ap-296	240	11	is	be	AUX
ap-296	240	12	examined	examine	VERB
ap-296	240	13	.	.	PUNCT
ap-296	241	1	the	the	DET
ap-296	241	2	simulation	simulation	NOUN
ap-296	241	3	results	result	NOUN
ap-296	241	4	of	of	ADP
ap-296	241	5	the	the	DET
ap-296	241	6	application	application	NOUN
ap-296	241	7	examples	example	NOUN
ap-296	241	8	indicate	indicate	VERB
ap-296	241	9	the	the	DET
ap-296	241	10	effectiveness	effectiveness	NOUN
ap-296	241	11	of	of	ADP
ap-296	241	12	the	the	DET
ap-296	241	13	proposed	propose	VERB
ap-296	241	14	laga	laga	NOUN
ap-296	241	15	approach	approach	NOUN
ap-296	241	16	as	as	ADP
ap-296	241	17	a	a	DET
ap-296	241	18	promising	promising	ADJ
ap-296	241	19	learning	learning	NOUN
ap-296	241	20	algorithm	algorithm	NOUN
ap-296	241	21	.	.	PUNCT
ap-296	242	1	acknowledgement	acknowledgement	NOUN
ap-296	242	2	this	this	DET
ap-296	242	3	work	work	NOUN
ap-296	242	4	received	receive	VERB
ap-296	242	5	support	support	NOUN
ap-296	242	6	from	from	ADP
ap-296	242	7	the	the	DET
ap-296	242	8	ministry	ministry	PROPN
ap-296	242	9	of	of	ADP
ap-296	242	10	education	education	NOUN
ap-296	242	11	of	of	ADP
ap-296	242	12	the	the	DET
ap-296	242	13	czech	czech	PROPN
ap-296	242	14	republic	republic	NOUN
ap-296	242	15	under	under	ADP
ap-296	242	16	project	project	NOUN
ap-296	242	17	ln00b096	ln00b096	NOUN
ap-296	242	18	.	.	PUNCT
ap-296	243	1	references	reference	NOUN
ap-296	243	2	[	[	X
ap-296	243	3	1	1	NUM
ap-296	243	4	]	]	PUNCT
ap-296	243	5	attia	attia	NOUN
ap-296	243	6	,	,	PUNCT
ap-296	243	7	a.	a.	NOUN
ap-296	243	8	:	:	PUNCT
ap-296	243	9	global	global	ADJ
ap-296	243	10	optimization	optimization	NOUN
ap-296	243	11	method	method	NOUN
ap-296	243	12	for	for	ADP
ap-296	243	13	neuro	neuro	NOUN
ap-296	243	14	-	-	PUNCT
ap-296	243	15	fuzzy	fuzzy	ADJ
ap-296	243	16	modeling	modeling	NOUN
ap-296	243	17	and	and	CCONJ
ap-296	243	18	identification	identification	NOUN
ap-296	243	19	.	.	PUNCT
ap-296	244	1	research	research	NOUN
ap-296	244	2	report	report	PROPN
ap-296	244	3	335/01/203	335/01/203	NUM
ap-296	244	4	,	,	PUNCT
ap-296	244	5	ctu	ctu	NOUN
ap-296	244	6	,	,	PUNCT
ap-296	244	7	faculty	faculty	NOUN
ap-296	244	8	of	of	ADP
ap-296	244	9	electrical	electrical	ADJ
ap-296	244	10	engineering	engineering	NOUN
ap-296	244	11	,	,	PUNCT
ap-296	244	12	department	department	NOUN
ap-296	244	13	of	of	ADP
ap-296	244	14	control	control	PROPN
ap-296	244	15	engineering	engineering	PROPN
ap-296	244	16	,	,	PUNCT
ap-296	244	17	prague	prague	PROPN
ap-296	244	18	,	,	PUNCT
ap-296	244	19	2001	2001	NUM
ap-296	244	20	,	,	PUNCT
ap-296	244	21	p.	p.	NOUN
ap-296	244	22	41	41	NUM
ap-296	245	1	[	[	X
ap-296	245	2	2	2	NUM
ap-296	245	3	]	]	PUNCT
ap-296	245	4	attia	attia	PROPN
ap-296	245	5	,	,	PUNCT
ap-296	245	6	a.	a.	NOUN
ap-296	245	7	,	,	PUNCT
ap-296	245	8	horáček	horáček	ADJ
ap-296	245	9	,	,	PUNCT
ap-296	245	10	p.	p.	NOUN
ap-296	245	11	:	:	PUNCT
ap-296	245	12	an	an	DET
ap-296	245	13	optimal	optimal	ADJ
ap-296	245	14	design	design	NOUN
ap-296	245	15	of	of	ADP
ap-296	245	16	a	a	DET
ap-296	245	17	fuzzy	fuzzy	ADJ
ap-296	245	18	logic	logic	NOUN
ap-296	245	19	neural	neural	ADJ
ap-296	245	20	network	network	NOUN
ap-296	245	21	using	use	VERB
ap-296	245	22	a	a	DET
ap-296	245	23	linear	linear	ADJ
ap-296	245	24	adapted	adapt	VERB
ap-296	245	25	genetic	genetic	ADJ
ap-296	245	26	algorithm	algorithm	NOUN
ap-296	245	27	.	.	PUNCT
ap-296	246	1	in	in	ADP
ap-296	246	2	:	:	PUNCT
ap-296	246	3	7th	7th	ADJ
ap-296	246	4	international	international	PROPN
ap-296	246	5	mendel	mendel	PROPN
ap-296	246	6	conference	conference	PROPN
ap-296	246	7	on	on	ADP
ap-296	246	8	soft	soft	ADJ
ap-296	246	9	computing	computing	NOUN
ap-296	246	10	,	,	PUNCT
ap-296	246	11	brno	brno	NOUN
ap-296	246	12	,	,	PUNCT
ap-296	246	13	2001	2001	NUM
ap-296	246	14	,	,	PUNCT
ap-296	246	15	pp	pp	ADJ
ap-296	246	16	.	.	PUNCT
ap-296	247	1	42–49	42–49	NUM
ap-296	248	1	[	[	X
ap-296	248	2	3	3	NUM
ap-296	248	3	]	]	PUNCT
ap-296	248	4	attia	attia	PROPN
ap-296	248	5	,	,	PUNCT
ap-296	248	6	a.	a.	NOUN
ap-296	248	7	,	,	PUNCT
ap-296	248	8	horáček	horáček	ADJ
ap-296	248	9	,	,	PUNCT
ap-296	248	10	p.	p.	NOUN
ap-296	248	11	:	:	PUNCT
ap-296	249	1	adaptation	adaptation	NOUN
ap-296	249	2	of	of	ADP
ap-296	249	3	genetic	genetic	ADJ
ap-296	249	4	algorithms	algorithm	NOUN
ap-296	249	5	for	for	ADP
ap-296	249	6	optimization	optimization	NOUN
ap-296	249	7	problem	problem	NOUN
ap-296	249	8	solving	solve	VERB
ap-296	249	9	.	.	PUNCT
ap-296	250	1	in	in	ADP
ap-296	250	2	:	:	PUNCT
ap-296	250	3	7th	7th	ADJ
ap-296	250	4	international	international	PROPN
ap-296	250	5	mendel	mendel	PROPN
ap-296	250	6	conference	conference	PROPN
ap-296	250	7	on	on	ADP
ap-296	250	8	soft	soft	ADJ
ap-296	250	9	computing	computing	NOUN
ap-296	250	10	,	,	PUNCT
ap-296	250	11	brno	brno	NOUN
ap-296	250	12	,	,	PUNCT
ap-296	250	13	2001	2001	NUM
ap-296	250	14	,	,	PUNCT
ap-296	250	15	pp	pp	ADJ
ap-296	250	16	.	.	PUNCT
ap-296	251	1	36–41	36–41	NUM
ap-296	252	1	[	[	SYM
ap-296	252	2	4	4	NUM
ap-296	252	3	]	]	X
ap-296	252	4	farag	farag	NOUN
ap-296	252	5	,	,	PUNCT
ap-296	252	6	w.	w.	PROPN
ap-296	252	7	,	,	PUNCT
ap-296	252	8	victor	victor	PROPN
ap-296	252	9	,	,	PUNCT
ap-296	252	10	h.	h.	PROPN
ap-296	252	11	:	:	PUNCT
ap-296	252	12	a	a	DET
ap-296	252	13	genetic	genetic	NOUN
ap-296	252	14	-	-	PUNCT
ap-296	252	15	based	base	VERB
ap-296	252	16	neuro	neuro	NOUN
ap-296	252	17	-	-	PUNCT
ap-296	252	18	fuzzy	fuzzy	ADJ
ap-296	252	19	approach	approach	NOUN
ap-296	252	20	for	for	ADP
ap-296	252	21	modeling	modeling	NOUN
ap-296	252	22	and	and	CCONJ
ap-296	252	23	control	control	NOUN
ap-296	252	24	of	of	ADP
ap-296	252	25	dynamical	dynamical	ADJ
ap-296	252	26	systems	system	NOUN
ap-296	252	27	.	.	PUNCT
ap-296	253	1	ieee	ieee	PROPN
ap-296	253	2	trans	trans	PROPN
ap-296	253	3	.	.	PUNCT
ap-296	254	1	on	on	ADP
ap-296	254	2	neural	neural	ADJ
ap-296	254	3	networks	network	NOUN
ap-296	254	4	.	.	PUNCT
ap-296	255	1	vol	vol	NOUN
ap-296	255	2	.	.	PROPN
ap-296	256	1	9	9	NUM
ap-296	256	2	,	,	PUNCT
ap-296	256	3	no	no	INTJ
ap-296	256	4	.	.	NOUN
ap-296	256	5	5	5	NUM
ap-296	256	6	,	,	PUNCT
ap-296	256	7	september	september	PROPN
ap-296	256	8	1998	1998	NUM
ap-296	257	1	[	[	X
ap-296	257	2	5	5	NUM
ap-296	257	3	]	]	X
ap-296	257	4	gen	gen	PROPN
ap-296	257	5	,	,	PUNCT
ap-296	257	6	m.	m.	NOUN
ap-296	257	7	,	,	PUNCT
ap-296	257	8	cheng	cheng	PROPN
ap-296	257	9	,	,	PUNCT
ap-296	257	10	r.	r.	PROPN
ap-296	257	11	:	:	PUNCT
ap-296	257	12	genetic	genetic	ADJ
ap-296	257	13	algorithms	algorithm	NOUN
ap-296	257	14	and	and	CCONJ
ap-296	257	15	engineering	engineering	NOUN
ap-296	257	16	optimizations	optimization	NOUN
ap-296	257	17	.	.	PUNCT
ap-296	258	1	john	john	PROPN
ap-296	258	2	wiley	wiley	PROPN
ap-296	258	3	&	&	CCONJ
ap-296	258	4	sons	son	NOUN
ap-296	258	5	,	,	PUNCT
ap-296	258	6	2000	2000	NUM
ap-296	259	1	[	[	X
ap-296	259	2	6	6	NUM
ap-296	259	3	]	]	X
ap-296	259	4	goldberg	goldberg	PROPN
ap-296	259	5	,	,	PUNCT
ap-296	259	6	d.	d.	PROPN
ap-296	259	7	:	:	PUNCT
ap-296	259	8	genetic	genetic	ADJ
ap-296	259	9	algorithms	algorithm	NOUN
ap-296	259	10	in	in	ADP
ap-296	259	11	search	search	NOUN
ap-296	259	12	,	,	PUNCT
ap-296	259	13	optimization	optimization	NOUN
ap-296	259	14	,	,	PUNCT
ap-296	259	15	and	and	CCONJ
ap-296	259	16	machine	machine	NOUN
ap-296	259	17	learning	learning	NOUN
ap-296	259	18	.	.	PUNCT
ap-296	260	1	addison	addison	PROPN
ap-296	260	2	–	–	PUNCT
ap-296	260	3	wesley	wesley	PROPN
ap-296	260	4	,	,	PUNCT
ap-296	260	5	1989	1989	NUM
ap-296	260	6	[	[	X
ap-296	260	7	7	7	NUM
ap-296	260	8	]	]	X
ap-296	260	9	horáček	horáček	ADJ
ap-296	260	10	,	,	PUNCT
ap-296	260	11	p.	p.	NOUN
ap-296	260	12	:	:	PUNCT
ap-296	260	13	fuzzy	fuzzy	ADJ
ap-296	260	14	modeling	modeling	NOUN
ap-296	260	15	and	and	CCONJ
ap-296	260	16	control	control	NOUN
ap-296	260	17	.	.	PUNCT
ap-296	261	1	in	in	ADP
ap-296	261	2	:	:	PUNCT
ap-296	261	3	h.	h.	PROPN
ap-296	261	4	adelsberger	adelsberger	PROPN
ap-296	261	5	,	,	PUNCT
ap-296	261	6	j.	j.	PROPN
ap-296	261	7	lažanaský	lažanaský	PROPN
ap-296	261	8	,	,	PUNCT
ap-296	261	9	v.	v.	PROPN
ap-296	261	10	ma	ma	PROPN
ap-296	261	11	ík	ík	NOUN
ap-296	261	12	(	(	PUNCT
ap-296	261	13	eds	ed	NOUN
ap-296	261	14	.	.	PROPN
ap-296	261	15	):	):	PUNCT
ap-296	261	16	information	information	NOUN
ap-296	261	17	management	management	NOUN
ap-296	261	18	in	in	ADP
ap-296	261	19	computer	computer	NOUN
ap-296	261	20	integrated	integrate	VERB
ap-296	261	21	manufacturing	manufacturing	NOUN
ap-296	261	22	.	.	PUNCT
ap-296	262	1	lecture	lecture	NOUN
ap-296	262	2	notes	note	NOUN
ap-296	262	3	in	in	ADP
ap-296	262	4	computer	computer	NOUN
ap-296	262	5	science	science	NOUN
ap-296	263	1	no	no	INTJ
ap-296	263	2	.	.	PROPN
ap-296	263	3	973	973	NUM
ap-296	263	4	,	,	PUNCT
ap-296	263	5	springer	springer	NOUN
ap-296	263	6	-	-	PUNCT
ap-296	263	7	verlag	verlag	PROPN
ap-296	263	8	berlin	berlin	PROPN
ap-296	263	9	,	,	PUNCT
ap-296	263	10	1995	1995	NUM
ap-296	263	11	,	,	PUNCT
ap-296	263	12	pp	pp	ADV
ap-296	263	13	.	.	PUNCT
ap-296	264	1	257–288	257–288	NUM
ap-296	264	2	[	[	X
ap-296	264	3	8	8	NUM
ap-296	264	4	]	]	X
ap-296	264	5	jang	jang	PROPN
ap-296	264	6	,	,	PUNCT
ap-296	264	7	s.	s.	PROPN
ap-296	264	8	r.	r.	PROPN
ap-296	264	9	,	,	PUNCT
ap-296	264	10	sun	sun	PROPN
ap-296	264	11	,	,	PUNCT
ap-296	264	12	c.	c.	PROPN
ap-296	264	13	t.	t.	PROPN
ap-296	264	14	,	,	PUNCT
ap-296	264	15	mizutani	mizutani	PROPN
ap-296	264	16	,	,	PUNCT
ap-296	264	17	e.	e.	PROPN
ap-296	264	18	:	:	PUNCT
ap-296	264	19	neuro	neuro	NOUN
ap-296	264	20	-	-	PUNCT
ap-296	264	21	fuzzy	fuzzy	ADJ
ap-296	264	22	and	and	CCONJ
ap-296	264	23	soft	soft	ADJ
ap-296	264	24	computing	computing	NOUN
ap-296	264	25	:	:	PUNCT
ap-296	264	26	a	a	DET
ap-296	264	27	computational	computational	ADJ
ap-296	264	28	approach	approach	NOUN
ap-296	264	29	to	to	ADP
ap-296	264	30	learning	learning	NOUN
ap-296	264	31	and	and	CCONJ
ap-296	264	32	machine	machine	NOUN
ap-296	264	33	intelligence	intelligence	NOUN
ap-296	264	34	.	.	PUNCT
ap-296	265	1	prentice	prentice	PROPN
ap-296	265	2	hall	hall	PROPN
ap-296	265	3	,	,	PUNCT
ap-296	265	4	inc	inc	PROPN
ap-296	265	5	.	.	PROPN
ap-296	265	6	,	,	PUNCT
ap-296	265	7	1997	1997	NUM
ap-296	266	1	[	[	X
ap-296	266	2	9	9	NUM
ap-296	266	3	]	]	X
ap-296	266	4	kolínský	kolínský	PROPN
ap-296	266	5	,	,	PUNCT
ap-296	266	6	j.	j.	PROPN
ap-296	266	7	:	:	PUNCT
ap-296	266	8	identifikace	identifikace	NOUN
ap-296	266	9	parametr	parametr	PROPN
ap-296	266	10	fuzzy−logických	fuzzy−logických	NUM
ap-296	266	11	neuronových	neuronových	NOUN
ap-296	266	12	sítí	sítí	PROPN
ap-296	266	13	.	.	PUNCT
ap-296	267	1	diplomová	diplomová	PROPN
ap-296	267	2	práce	práce	PROPN
ap-296	267	3	,	,	PUNCT
ap-296	267	4	katedra	katedra	PROPN
ap-296	267	5	řídicí	řídicí	ADV
ap-296	267	6	techniky	techniky	PROPN
ap-296	267	7	,	,	PUNCT
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ap-296	267	11	čvut	čvut	PROPN
ap-296	267	12	,	,	PUNCT
ap-296	267	13	2000	2000	NUM
ap-296	268	1	[	[	X
ap-296	268	2	10	10	NUM
ap-296	268	3	]	]	X
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ap-296	268	5	,	,	PUNCT
ap-296	268	6	c.	c.	PROPN
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ap-296	268	13	:	:	PUNCT
ap-296	269	1	neural	neural	ADJ
ap-296	269	2	–	–	PUNCT
ap-296	269	3	network	network	NOUN
ap-296	269	4	-	-	PUNCT
ap-296	269	5	based	base	VERB
ap-296	269	6	fuzzy	fuzzy	ADJ
ap-296	269	7	logic	logic	NOUN
ap-296	269	8	control	control	NOUN
ap-296	269	9	and	and	CCONJ
ap-296	269	10	design	design	NOUN
ap-296	269	11	decision	decision	NOUN
ap-296	269	12	system	system	NOUN
ap-296	269	13	.	.	PUNCT
ap-296	270	1	ieee	ieee	PROPN
ap-296	270	2	trans	trans	PROPN
ap-296	270	3	.	.	PUNCT
ap-296	271	1	on	on	ADP
ap-296	271	2	computers	computer	NOUN
ap-296	271	3	,	,	PUNCT
ap-296	271	4	vol	vol	NOUN
ap-296	271	5	.	.	PROPN
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ap-296	271	7	,	,	PUNCT
ap-296	271	8	no	no	INTJ
ap-296	271	9	.	.	PUNCT
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ap-296	271	11	,	,	PUNCT
ap-296	271	12	pp	pp	ADP
ap-296	271	13	.	.	PUNCT
ap-296	272	1	1320–1336	1320–1336	NUM
ap-296	273	1	[	[	X
ap-296	273	2	11	11	NUM
ap-296	273	3	]	]	X
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ap-296	273	5	,	,	PUNCT
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ap-296	273	7	,	,	PUNCT
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ap-296	273	9	,	,	PUNCT
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ap-296	273	11	,	,	PUNCT
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ap-296	273	13	,	,	PUNCT
ap-296	273	14	p.	p.	NOUN
ap-296	273	15	:	:	PUNCT
ap-296	273	16	in	in	ADP
ap-296	273	17	:	:	PUNCT
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ap-296	273	19	of	of	ADP
ap-296	273	20	fuzzy	fuzzy	ADJ
ap-296	273	21	and	and	CCONJ
ap-296	273	22	neuro	neuro	NOUN
ap-296	273	23	-	-	PUNCT
ap-296	273	24	fuzzy	fuzzy	ADJ
ap-296	273	25	systems	system	NOUN
ap-296	273	26	by	by	ADP
ap-296	273	27	means	mean	NOUN
ap-296	273	28	of	of	ADP
ap-296	273	29	adaptive	adaptive	ADJ
ap-296	273	30	genetic	genetic	ADJ
ap-296	273	31	search	search	NOUN
ap-296	273	32	.	.	PUNCT
ap-296	274	1	proc	proc	NOUN
ap-296	274	2	.	.	PUNCT
ap-296	275	1	of	of	ADP
ap-296	275	2	ga+se’96	ga+se’96	PROPN
ap-296	275	3	ic	ic	PROPN
ap-296	275	4	,	,	PUNCT
ap-296	275	5	gursuf	gursuf	PROPN
ap-296	275	6	,	,	PUNCT
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ap-296	275	8	,	,	PUNCT
ap-296	275	9	1996	1996	NUM
ap-296	275	10	[	[	X
ap-296	275	11	12	12	NUM
ap-296	275	12	]	]	X
ap-296	275	13	narendra	narendra	PROPN
ap-296	275	14	,	,	PUNCT
ap-296	275	15	k.	k.	PROPN
ap-296	275	16	s.	s.	PROPN
ap-296	275	17	,	,	PUNCT
ap-296	275	18	parthasarathy	parthasarathy	PROPN
ap-296	275	19	,	,	PUNCT
ap-296	275	20	k.	k.	NOUN
ap-296	275	21	:	:	PUNCT
ap-296	275	22	identification	identification	NOUN
ap-296	275	23	and	and	CCONJ
ap-296	275	24	control	control	NOUN
ap-296	275	25	of	of	ADP
ap-296	275	26	dynamical	dynamical	ADJ
ap-296	275	27	systems	system	NOUN
ap-296	275	28	using	use	VERB
ap-296	275	29	neural	neural	ADJ
ap-296	275	30	networks	network	NOUN
ap-296	275	31	.	.	PUNCT
ap-296	276	1	ieee	ieee	PROPN
ap-296	276	2	trans	trans	PROPN
ap-296	276	3	.	.	PUNCT
ap-296	277	1	neural	neural	ADJ
ap-296	277	2	networks	network	NOUN
ap-296	277	3	,	,	PUNCT
ap-296	277	4	vol	vol	NOUN
ap-296	277	5	.	.	PROPN
ap-296	277	6	1	1	NUM
ap-296	277	7	,	,	PUNCT
ap-296	277	8	1990	1990	NUM
ap-296	277	9	[	[	SYM
ap-296	277	10	13	13	NUM
ap-296	277	11	]	]	X
ap-296	277	12	srinivas	srinivas	PROPN
ap-296	277	13	,	,	PUNCT
ap-296	277	14	m.	m.	NOUN
ap-296	277	15	,	,	PUNCT
ap-296	277	16	patnaik	patnaik	PROPN
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ap-296	277	18	l.	l.	PROPN
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ap-296	277	21	in	in	ADP
ap-296	277	22	:	:	PUNCT
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ap-296	277	24	probabilities	probability	NOUN
ap-296	277	25	of	of	ADP
ap-296	277	26	crossover	crossover	NOUN
ap-296	277	27	and	and	CCONJ
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ap-296	277	29	in	in	ADP
ap-296	277	30	genetic	genetic	ADJ
ap-296	277	31	algorithms	algorithm	NOUN
ap-296	277	32	.	.	PUNCT
ap-296	278	1	ieee	ieee	PROPN
ap-296	278	2	trans	trans	PROPN
ap-296	278	3	.	.	PUNCT
ap-296	279	1	system	system	NOUN
ap-296	279	2	.	.	PUNCT
ap-296	280	1	man	man	NOUN
ap-296	280	2	.	.	PUNCT
ap-296	281	1	and	and	CCONJ
ap-296	281	2	cybernetics	cybernetic	NOUN
ap-296	281	3	,	,	PUNCT
ap-296	281	4	vol	vol	NOUN
ap-296	281	5	.	.	PROPN
ap-296	282	1	24	24	NUM
ap-296	282	2	,	,	PUNCT
ap-296	282	3	no	no	INTJ
ap-296	282	4	.	.	PUNCT
ap-296	283	1	4/1994	4/1994	NUM
ap-296	283	2	,	,	PUNCT
ap-296	283	3	pp	pp	X
ap-296	283	4	.	.	PUNCT
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ap-296	284	2	[	[	X
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ap-296	284	4	]	]	X
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ap-296	284	6	,	,	PUNCT
ap-296	284	7	m.	m.	NOUN
ap-296	284	8	,	,	PUNCT
ap-296	284	9	patnaik	patnaik	PROPN
ap-296	284	10	,	,	PUNCT
ap-296	284	11	l.	l.	PROPN
ap-296	284	12	m.	m.	PROPN
ap-296	284	13	:	:	PUNCT
ap-296	284	14	in	in	ADP
ap-296	284	15	:	:	PUNCT
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ap-296	284	17	search	search	NOUN
ap-296	284	18	:	:	PUNCT
ap-296	284	19	analysis	analysis	NOUN
ap-296	284	20	using	use	VERB
ap-296	284	21	fitness	fitness	NOUN
ap-296	284	22	moments	moment	NOUN
ap-296	284	23	.	.	PUNCT
ap-296	285	1	ieee	ieee	PROPN
ap-296	285	2	trans	trans	PROPN
ap-296	285	3	.	.	PUNCT
ap-296	286	1	on	on	ADP
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ap-296	286	3	and	and	CCONJ
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ap-296	286	5	engineering	engineering	NOUN
ap-296	286	6	.	.	PUNCT
ap-296	287	1	vol	vol	NOUN
ap-296	287	2	.	.	PROPN
ap-296	287	3	8	8	NUM
ap-296	287	4	,	,	PUNCT
ap-296	287	5	no	no	INTJ
ap-296	287	6	.	.	PUNCT
ap-296	288	1	1/1996	1/1996	NUM
ap-296	289	1	[	[	X
ap-296	289	2	15	15	NUM
ap-296	289	3	]	]	X
ap-296	289	4	wang	wang	PROPN
ap-296	289	5	li	li	PROPN
ap-296	289	6	-	-	PROPN
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ap-296	289	8	:	:	PUNCT
ap-296	289	9	in	in	ADP
ap-296	289	10	:	:	PUNCT
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ap-296	289	12	fuzzy	fuzzy	ADJ
ap-296	289	13	systems	system	NOUN
ap-296	289	14	and	and	CCONJ
ap-296	289	15	control	control	NOUN
ap-296	289	16	,	,	PUNCT
ap-296	289	17	design	design	NOUN
ap-296	289	18	and	and	CCONJ
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ap-296	289	21	.	.	PUNCT
ap-296	290	1	by	by	ADP
ap-296	290	2	ptr	ptr	PROPN
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ap-296	290	5	,	,	PUNCT
ap-296	290	6	1994	1994	NUM
ap-296	290	7	[	[	X
ap-296	290	8	16	16	NUM
ap-296	290	9	]	]	X
ap-296	290	10	wang	wang	PROPN
ap-296	290	11	li	li	PROPN
ap-296	290	12	-	-	PROPN
ap-296	290	13	xin	xin	PROPN
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ap-296	290	19	in	in	ADP
ap-296	290	20	:	:	PUNCT
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ap-296	290	24	by	by	ADP
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ap-296	290	28	.	.	PUNCT
ap-296	291	1	ieee	ieee	PROPN
ap-296	291	2	trans	trans	PROPN
ap-296	291	3	.	.	PUNCT
ap-296	292	1	systems	system	NOUN
ap-296	292	2	,	,	PUNCT
ap-296	292	3	man	man	NOUN
ap-296	292	4	,	,	PUNCT
ap-296	292	5	and	and	CCONJ
ap-296	292	6	cybernetics	cybernetic	NOUN
ap-296	292	7	,	,	PUNCT
ap-296	292	8	vol	vol	NOUN
ap-296	292	9	.	.	PROPN
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ap-296	292	11	,	,	PUNCT
ap-296	292	12	no	no	INTJ
ap-296	292	13	.	.	PUNCT
ap-296	293	1	6/1992	6/1992	NUM
ap-296	293	2	,	,	PUNCT
ap-296	293	3	pp	pp	ADV
ap-296	293	4	.	.	PUNCT
ap-296	294	1	1414–1427	1414–1427	NUM
ap-296	294	2	[	[	X
ap-296	294	3	17	17	NUM
ap-296	294	4	]	]	X
ap-296	294	5	winter	winter	NOUN
ap-296	294	6	,	,	PUNCT
ap-296	294	7	g.	g.	PROPN
ap-296	294	8	,	,	PUNCT
ap-296	294	9	périanx	périanx	NOUN
ap-296	294	10	,	,	PUNCT
ap-296	294	11	j.	j.	PROPN
ap-296	294	12	,	,	PUNCT
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ap-296	294	14	,	,	PUNCT
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ap-296	294	16	,	,	PUNCT
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ap-296	295	2	algorithms	algorithm	NOUN
ap-296	295	3	in	in	ADP
ap-296	295	4	engineering	engineering	NOUN
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ap-296	295	6	computer	computer	NOUN
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ap-296	295	8	.	.	PUNCT
ap-296	296	1	isbn	isbn	ADJ
ap-296	296	2	04	04	NUM
ap-296	296	3	-	-	SYM
ap-296	296	4	71	71	NUM
ap-296	296	5	-	-	PUNCT
ap-296	296	6	95859	95859	NUM
ap-296	296	7	-	-	PUNCT
ap-296	296	8	x	x	NOUN
ap-296	296	9	,	,	PUNCT
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ap-296	296	13	sons	son	NOUN
ap-296	296	14	,	,	PUNCT
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ap-296	296	16	ing	ing	NOUN
ap-296	296	17	.	.	PUNCT
ap-296	297	1	abdel	abdel	PROPN
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ap-296	297	8	:	:	PUNCT
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ap-296	297	10	2	2	NUM
ap-296	297	11	2435	2435	NUM
ap-296	297	12	7612	7612	NUM
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ap-296	297	14	:	:	PUNCT
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ap-296	297	16	2	2	NUM
ap-296	297	17	2435	2435	NUM
ap-296	297	18	7298	7298	NUM
ap-296	298	1	e	e	NOUN
ap-296	298	2	-	-	NOUN
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ap-296	298	4	:	:	PUNCT
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ap-296	299	1	ing	ing	PROPN
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ap-296	299	36	27	27	NUM
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ap-296	299	38	6	6	NUM
ap-296	299	39	,	,	PUNCT
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ap-296	299	42	76	76	NUM
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ap-296	300	2	czech	czech	PROPN
ap-296	300	3	technical	technical	PROPN
ap-296	300	4	university	university	PROPN
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ap-296	300	6	house	house	NOUN
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ap-296	302	2	0	0	NUM
ap-296	302	3	100	100	NUM
ap-296	302	4	200	200	NUM
ap-296	302	5	300	300	NUM
ap-296	302	6	400	400	NUM
ap-296	302	7	500	500	NUM
ap-296	302	8	600	600	NUM
ap-296	302	9	700	700	NUM
ap-296	303	1	-6	-6	ADP
ap-296	303	2	-4	-4	INTJ
ap-296	304	1	-2	-2	NOUN
ap-296	304	2	0	0	NUM
ap-296	304	3	2	2	NUM
ap-296	304	4	4	4	NUM
ap-296	304	5	6	6	NUM
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ap-296	305	1	14	14	NUM
ap-296	305	2	:	:	PUNCT
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ap-296	305	31	�	�	PROPN
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ap-296	305	35	0	0	NUM
ap-296	305	36	5	5	NUM
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ap-296	305	39	.	.	PUNCT
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ap-296	306	8	k	k	NOUN
ap-296	306	9	700	700	NUM
ap-296	306	10	,	,	PUNCT
ap-296	306	11	and	and	CCONJ
ap-296	306	12	�	�	PROPN
ap-296	306	13	�	�	PROPN
ap-296	306	14	�	�	PROPN
ap-296	306	15	�	�	PROPN
ap-296	306	16	�	�	PROPN
ap-296	306	17	�	�	PROPN
ap-296	306	18	u	u	PROPN
ap-296	306	19	k	k	PROPN
ap-296	306	20	k	k	PROPN
ap-296	306	21	k	k	PROPN
ap-296	306	22	�	�	PROPN
ap-296	306	23	�	�	PROPN
ap-296	306	24	�	�	PROPN
ap-296	306	25	�	�	PROPN
ap-296	306	26	0	0	NUM
ap-296	306	27	5	5	NUM
ap-296	306	28	2	2	NUM
ap-296	306	29	250	250	NUM
ap-296	306	30	0	0	NUM
ap-296	306	31	5	5	NUM
ap-296	306	32	2	2	NUM
ap-296	306	33	25	25	NUM
ap-296	306	34	25	25	NUM
ap-296	306	35	.	.	PUNCT
ap-296	307	1	sin	sin	NOUN
ap-296	307	2	.	.	PUNCT
ap-296	308	1	sin	sin	PROPN
ap-296	308	2	�	�	PROPN
ap-296	308	3	�	�	PROPN
ap-296	308	4	for	for	ADP
ap-296	308	5	1	1	NUM
ap-296	308	6	k	k	SYM
ap-296	308	7	500	500	NUM
ap-296	308	8	table	table	NOUN
ap-296	308	9	of	of	ADP
ap-296	308	10	contents	content	NOUN
ap-296	308	11	power	power	NOUN
ap-296	308	12	characteristics	characteristic	NOUN
ap-296	308	13	of	of	ADP
ap-296	308	14	a	a	DET
ap-296	308	15	screw	screw	NOUN
ap-296	308	16	agitator	agitator	NOUN
ap-296	308	17	in	in	ADP
ap-296	308	18	a	a	DET
ap-296	308	19	tube	tube	NOUN
ap-296	308	20	3	3	NUM
ap-296	308	21	f.	f.	PROPN
ap-296	308	22	rieger	rieger	PROPN
ap-296	308	23	study	study	PROPN
ap-296	308	24	of	of	ADP
ap-296	308	25	the	the	DET
ap-296	308	26	blending	blend	VERB
ap-296	308	27	efficiency	efficiency	NOUN
ap-296	308	28	of	of	ADP
ap-296	308	29	pitched	pitch	VERB
ap-296	308	30	blade	blade	NOUN
ap-296	308	31	impellers	impeller	NOUN
ap-296	308	32	7	7	NUM
ap-296	308	33	i.	i.	NOUN
ap-296	308	34	foøt	foøt	PROPN
ap-296	308	35	,	,	PUNCT
ap-296	308	36	t.	t.	PROPN
ap-296	308	37	jirout	jirout	PROPN
ap-296	308	38	,	,	PUNCT
ap-296	308	39	f.	f.	PROPN
ap-296	308	40	rieger	rieger	PROPN
ap-296	308	41	,	,	PUNCT
ap-296	308	42	r.	r.	PROPN
ap-296	308	43	allner	allner	PROPN
ap-296	308	44	,	,	PUNCT
ap-296	309	1	r.	r.	PROPN
ap-296	309	2	sperling	sperling	PROPN
ap-296	309	3	a	a	DET
ap-296	309	4	geothermal	geothermal	ADJ
ap-296	309	5	energy	energy	NOUN
ap-296	309	6	supported	support	VERB
ap-296	309	7	gas	gas	NOUN
ap-296	309	8	-	-	PUNCT
ap-296	309	9	steam	steam	NOUN
ap-296	309	10	cogeneration	cogeneration	NOUN
ap-296	309	11	unit	unit	NOUN
ap-296	309	12	as	as	ADP
ap-296	309	13	a	a	DET
ap-296	309	14	possible	possible	ADJ
ap-296	309	15	replacement	replacement	NOUN
ap-296	309	16	for	for	ADP
ap-296	309	17	the	the	DET
ap-296	309	18	old	old	ADJ
ap-296	309	19	part	part	NOUN
ap-296	309	20	of	of	ADP
ap-296	309	21	a	a	DET
ap-296	309	22	municipal	municipal	ADJ
ap-296	309	23	chp	chp	NOUN
ap-296	309	24	plant	plant	NOUN
ap-296	309	25	(	(	PUNCT
ap-296	309	26	teko	teko	PROPN
ap-296	309	27	)	)	PUNCT
ap-296	309	28	14	14	NUM
ap-296	309	29	l.	l.	PROPN
ap-296	309	30	böszörményi	böszörményi	PROPN
ap-296	309	31	,	,	PUNCT
ap-296	309	32	g.	g.	PROPN
ap-296	309	33	böszörményi	böszörményi	PROPN
ap-296	309	34	power	power	NOUN
ap-296	309	35	input	input	NOUN
ap-296	309	36	of	of	ADP
ap-296	309	37	high	high	ADJ
ap-296	309	38	-	-	PUNCT
ap-296	309	39	speed	speed	NOUN
ap-296	309	40	rotary	rotary	NOUN
ap-296	309	41	impellers	impeller	NOUN
ap-296	309	42	18	18	NUM
ap-296	309	43	k.	k.	PROPN
ap-296	309	44	r.	r.	PROPN
ap-296	309	45	beshay	beshay	PROPN
ap-296	309	46	,	,	PUNCT
ap-296	309	47	j.	j.	PROPN
ap-296	309	48	kratìna	kratìna	PROPN
ap-296	309	49	,	,	PUNCT
ap-296	309	50	i.	i.	PROPN
ap-296	309	51	foøt	foøt	PROPN
ap-296	309	52	,	,	PUNCT
ap-296	309	53	o.	o.	PROPN
ap-296	309	54	brùha	brùha	PROPN
ap-296	309	55	parasitic	parasitic	ADJ
ap-296	309	56	events	event	NOUN
ap-296	309	57	in	in	ADP
ap-296	309	58	envelope	envelope	NOUN
ap-296	309	59	analysis	analysis	NOUN
ap-296	309	60	24	24	NUM
ap-296	309	61	j.	j.	PROPN
ap-296	309	62	doubek	doubek	PROPN
ap-296	309	63	,	,	PUNCT
ap-296	309	64	m.	m.	NOUN
ap-296	309	65	kreidl	kreidl	PROPN
ap-296	309	66	stellar	stellar	ADJ
ap-296	309	67	image	image	NOUN
ap-296	309	68	interpretation	interpretation	NOUN
ap-296	309	69	system	system	NOUN
ap-296	309	70	using	use	VERB
ap-296	309	71	artificial	artificial	ADJ
ap-296	309	72	neural	neural	ADJ
ap-296	309	73	networks	network	NOUN
ap-296	309	74	:	:	PUNCT
ap-296	309	75	unipolar	unipolar	ADJ
ap-296	309	76	function	function	NOUN
ap-296	309	77	case	case	NOUN
ap-296	309	78	33	33	NUM
ap-296	309	79	f.	f.	PROPN
ap-296	309	80	i.	i.	PROPN
ap-296	309	81	younis	younis	PROPN
ap-296	309	82	,	,	PUNCT
ap-296	309	83	a.	a.	PROPN
ap-296	309	84	el	el	PROPN
ap-296	309	85	-	-	PROPN
ap-296	309	86	bassuny	bassuny	PROPN
ap-296	309	87	alawy	alawy	PROPN
ap-296	309	88	,	,	PUNCT
ap-296	309	89	b.	b.	PROPN
ap-296	309	90	šimák	šimák	PROPN
ap-296	309	91	,	,	PUNCT
ap-296	309	92	m.	m.	PROPN
ap-296	309	93	s.	s.	PROPN
ap-296	309	94	ella	ella	PROPN
ap-296	309	95	,	,	PUNCT
ap-296	310	1	m.	m.	PROPN
ap-296	310	2	a.	a.	PROPN
ap-296	310	3	madkour	madkour	PROPN
ap-296	310	4	deriving	deriving	NOUN
ap-296	310	5	triggers	trigger	NOUN
ap-296	310	6	from	from	ADP
ap-296	310	7	integrity	integrity	NOUN
ap-296	310	8	constraint	constraint	NOUN
ap-296	310	9	specifications	specification	NOUN
ap-296	310	10	in	in	ADP
ap-296	310	11	the	the	DET
ap-296	310	12	database	database	NOUN
ap-296	310	13	management	management	NOUN
ap-296	310	14	systems	system	NOUN
ap-296	310	15	39	39	NUM
ap-296	310	16	m.	m.	NOUN
ap-296	310	17	badawy	badawy	PROPN
ap-296	310	18	,	,	PUNCT
ap-296	310	19	k.	k.	PROPN
ap-296	310	20	richta	richta	VERB
ap-296	310	21	analytical	analytical	ADJ
ap-296	310	22	model	model	NOUN
ap-296	310	23	of	of	ADP
ap-296	310	24	modified	modify	VERB
ap-296	310	25	traffic	traffic	NOUN
ap-296	310	26	control	control	NOUN
ap-296	310	27	in	in	ADP
ap-296	310	28	an	an	DET
ap-296	310	29	atm	atm	NOUN
ap-296	310	30	computer	computer	NOUN
ap-296	310	31	network	network	NOUN
ap-296	310	32	45	45	NUM
ap-296	310	33	j.	j.	PROPN
ap-296	310	34	filip	filip	PROPN
ap-296	310	35	models	model	NOUN
ap-296	310	36	of	of	ADP
ap-296	310	37	financing	financing	NOUN
ap-296	310	38	and	and	CCONJ
ap-296	310	39	available	available	ADJ
ap-296	310	40	financial	financial	ADJ
ap-296	310	41	resources	resource	NOUN
ap-296	310	42	for	for	ADP
ap-296	310	43	transport	transport	NOUN
ap-296	310	44	infrastructure	infrastructure	NOUN
ap-296	310	45	projects	project	VERB
ap-296	310	46	51	51	NUM
ap-296	310	47	o.	o.	NOUN
ap-296	310	48	pokorná	pokorná	PROPN
ap-296	310	49	,	,	PUNCT
ap-296	310	50	d.	d.	PROPN
ap-296	310	51	mocková	mocková	PROPN
ap-296	310	52	measurement	measurement	PROPN
ap-296	310	53	of	of	ADP
ap-296	310	54	temperature	temperature	NOUN
ap-296	310	55	fields	field	NOUN
ap-296	310	56	in	in	ADP
ap-296	310	57	long	long	ADJ
ap-296	310	58	span	span	NOUN
ap-296	310	59	concrete	concrete	ADJ
ap-296	310	60	bridges	bridge	NOUN
ap-296	310	61	54	54	NUM
ap-296	310	62	j.	j.	PROPN
ap-296	310	63	øímal	øímal	PROPN
ap-296	310	64	non	non	ADJ
ap-296	310	65	-	-	ADJ
ap-296	310	66	linear	linear	ADJ
ap-296	310	67	temperature	temperature	NOUN
ap-296	310	68	profiles	profile	NOUN
ap-296	310	69	66	66	NUM
ap-296	310	70	t.	t.	PROPN
ap-296	310	71	ficker	ficker	PROPN
ap-296	310	72	,	,	PUNCT
ap-296	310	73	j.	j.	PROPN
ap-296	310	74	myslín	myslín	PROPN
ap-296	310	75	,	,	PUNCT
ap-296	310	76	z.	z.	PROPN
ap-296	310	77	podešvová	podešvová	PROPN
ap-296	310	78	modeling	model	VERB
ap-296	310	79	nonlinear	nonlinear	ADJ
ap-296	310	80	systems	system	NOUN
ap-296	310	81	by	by	ADP
ap-296	310	82	a	a	DET
ap-296	310	83	fuzzy	fuzzy	ADJ
ap-296	310	84	logic	logic	NOUN
ap-296	310	85	neural	neural	ADJ
ap-296	310	86	network	network	NOUN
ap-296	310	87	using	use	VERB
ap-296	310	88	genetic	genetic	ADJ
ap-296	310	89	algorithms	algorithm	NOUN
ap-296	310	90	69	69	NUM
ap-296	310	91	abdel	abdel	PROPN
ap-296	310	92	-	-	PUNCT
ap-296	310	93	fattah	fattah	PROPN
ap-296	310	94	attia	attia	NOUN
ap-296	310	95	,	,	PUNCT
ap-296	310	96	p.	p.	NOUN
ap-296	310	97	horáèek	horáèek	NOUN