id	sid	tid	token	lemma	pos
hjic-103	1	1	hungarian	hungarian	ADJ
hjic-103	1	2	journal	journal	NOUN
hjic-103	1	3	of	of	ADP
hjic-103	1	4	industrial	industrial	ADJ
hjic-103	1	5	chemistry	chemistry	NOUN
hjic-103	1	6	veszprém	veszprém	NOUN
hjic-103	1	7	vol	vol	NOUN
hjic-103	1	8	.	.	PUNCT
hjic-103	2	1	33(1	33(1	NUM
hjic-103	2	2	-	-	SYM
hjic-103	2	3	2	2	NUM
hjic-103	2	4	)	)	PUNCT
hjic-103	2	5	.	.	PUNCT
hjic-103	3	1	pp	pp	ADJ
hjic-103	3	2	.	.	PUNCT
hjic-103	4	1	113	113	NUM
hjic-103	4	2	-	-	SYM
hjic-103	4	3	117	117	NUM
hjic-103	4	4	.	.	PUNCT
hjic-103	5	1	(	(	PUNCT
hjic-103	5	2	2005	2005	NUM
hjic-103	5	3	)	)	PUNCT
hjic-103	5	4	evolutionary	evolutionary	ADJ
hjic-103	5	5	strategy	strategy	NOUN
hjic-103	5	6	in	in	ADP
hjic-103	5	7	iterative	iterative	NOUN
hjic-103	5	8	experiment	experiment	NOUN
hjic-103	5	9	design	design	NOUN
hjic-103	5	10	j.	j.	PROPN
hjic-103	5	11	madár	madár	PROPN
hjic-103	5	12	,	,	PUNCT
hjic-103	5	13	b.	b.	PROPN
hjic-103	5	14	balaskó	balaskó	PROPN
hjic-103	5	15	,	,	PUNCT
hjic-103	5	16	f.	f.	PROPN
hjic-103	5	17	szeifert	szeifert	PROPN
hjic-103	5	18	and	and	CCONJ
hjic-103	5	19	j.	j.	PROPN
hjic-103	5	20	abonyi	abonyi	PROPN
hjic-103	5	21	*	*	PROPN
hjic-103	5	22	department	department	PROPN
hjic-103	5	23	of	of	ADP
hjic-103	5	24	process	process	NOUN
hjic-103	5	25	engineering	engineering	NOUN
hjic-103	5	26	,	,	PUNCT
hjic-103	5	27	university	university	NOUN
hjic-103	5	28	of	of	ADP
hjic-103	5	29	veszprém	veszprém	NOUN
hjic-103	5	30	,	,	PUNCT
hjic-103	5	31	veszprém	veszprém	NOUN
hjic-103	5	32	,	,	PUNCT
hjic-103	5	33	egyetem	egyetem	PROPN
hjic-103	5	34	u.	u.	PROPN
hjic-103	5	35	10	10	NUM
hjic-103	5	36	,	,	PUNCT
hjic-103	5	37	h-8200	h-8200	PROPN
hjic-103	5	38	,	,	PUNCT
hjic-103	5	39	hungary	hungary	NOUN
hjic-103	5	40	,	,	PUNCT
hjic-103	5	41	www.fmt.vein.hu/softcomp	www.fmt.vein.hu/softcomp	PROPN
hjic-103	5	42	,	,	PUNCT
hjic-103	5	43	abonyij@fmt.vein.hu	abonyij@fmt.vein.hu	PROPN
hjic-103	5	44	process	process	NOUN
hjic-103	5	45	models	model	NOUN
hjic-103	5	46	play	play	VERB
hjic-103	5	47	important	important	ADJ
hjic-103	5	48	role	role	NOUN
hjic-103	5	49	in	in	ADP
hjic-103	5	50	computer	computer	NOUN
hjic-103	5	51	aided	aid	VERB
hjic-103	5	52	process	process	NOUN
hjic-103	5	53	engineering	engineering	NOUN
hjic-103	5	54	,	,	PUNCT
hjic-103	5	55	since	since	SCONJ
hjic-103	5	56	most	most	ADJ
hjic-103	5	57	of	of	ADP
hjic-103	5	58	advanced	advanced	ADJ
hjic-103	5	59	process	process	NOUN
hjic-103	5	60	monitoring	monitoring	NOUN
hjic-103	5	61	,	,	PUNCT
hjic-103	5	62	control	control	NOUN
hjic-103	5	63	,	,	PUNCT
hjic-103	5	64	and	and	CCONJ
hjic-103	5	65	optimization	optimization	NOUN
hjic-103	5	66	algorithms	algorithm	NOUN
hjic-103	5	67	relay	relay	NOUN
hjic-103	5	68	on	on	ADP
hjic-103	5	69	a	a	DET
hjic-103	5	70	model	model	NOUN
hjic-103	5	71	of	of	ADP
hjic-103	5	72	the	the	DET
hjic-103	5	73	process	process	NOUN
hjic-103	5	74	.	.	PUNCT
hjic-103	6	1	in	in	ADP
hjic-103	6	2	most	most	ADJ
hjic-103	6	3	of	of	ADP
hjic-103	6	4	the	the	DET
hjic-103	6	5	cases	case	NOUN
hjic-103	6	6	,	,	PUNCT
hjic-103	6	7	some	some	DET
hjic-103	6	8	parameters	parameter	NOUN
hjic-103	6	9	of	of	ADP
hjic-103	6	10	the	the	DET
hjic-103	6	11	model	model	NOUN
hjic-103	6	12	should	should	AUX
hjic-103	6	13	be	be	AUX
hjic-103	6	14	estimated	estimate	VERB
hjic-103	6	15	based	base	VERB
hjic-103	6	16	on	on	ADP
hjic-103	6	17	some	some	DET
hjic-103	6	18	experiments	experiment	NOUN
hjic-103	6	19	.	.	PUNCT
hjic-103	7	1	one	one	NUM
hjic-103	7	2	of	of	ADP
hjic-103	7	3	the	the	DET
hjic-103	7	4	factors	factor	NOUN
hjic-103	7	5	affecting	affect	VERB
hjic-103	7	6	the	the	DET
hjic-103	7	7	model	model	NOUN
hjic-103	7	8	prediction	prediction	NOUN
hjic-103	7	9	quality	quality	NOUN
hjic-103	7	10	is	be	AUX
hjic-103	7	11	the	the	DET
hjic-103	7	12	accuracy	accuracy	NOUN
hjic-103	7	13	of	of	ADP
hjic-103	7	14	these	these	DET
hjic-103	7	15	estimated	estimate	VERB
hjic-103	7	16	parameters	parameter	NOUN
hjic-103	7	17	.	.	PUNCT
hjic-103	8	1	establishing	establish	VERB
hjic-103	8	2	optimal	optimal	ADJ
hjic-103	8	3	experiment	experiment	NOUN
hjic-103	8	4	design	design	NOUN
hjic-103	8	5	can	can	AUX
hjic-103	8	6	maximise	maximise	VERB
hjic-103	8	7	the	the	DET
hjic-103	8	8	confidence	confidence	NOUN
hjic-103	8	9	on	on	ADP
hjic-103	8	10	the	the	DET
hjic-103	8	11	parameters	parameter	NOUN
hjic-103	8	12	,	,	PUNCT
hjic-103	8	13	hereby	hereby	ADV
hjic-103	8	14	increasing	increase	VERB
hjic-103	8	15	the	the	DET
hjic-103	8	16	confidence	confidence	NOUN
hjic-103	8	17	on	on	ADP
hjic-103	8	18	the	the	DET
hjic-103	8	19	model	model	NOUN
hjic-103	8	20	prediction	prediction	NOUN
hjic-103	8	21	.	.	PUNCT
hjic-103	9	1	the	the	DET
hjic-103	9	2	aim	aim	NOUN
hjic-103	9	3	of	of	ADP
hjic-103	9	4	this	this	DET
hjic-103	9	5	paper	paper	NOUN
hjic-103	9	6	is	be	AUX
hjic-103	9	7	to	to	PART
hjic-103	9	8	work	work	VERB
hjic-103	9	9	out	out	ADP
hjic-103	9	10	a	a	DET
hjic-103	9	11	modern	modern	ADJ
hjic-103	9	12	experiment	experiment	NOUN
hjic-103	9	13	design	design	NOUN
hjic-103	9	14	tool	tool	NOUN
hjic-103	9	15	to	to	PART
hjic-103	9	16	minimize	minimize	VERB
hjic-103	9	17	the	the	DET
hjic-103	9	18	number	number	NOUN
hjic-103	9	19	of	of	ADP
hjic-103	9	20	experiments	experiment	NOUN
hjic-103	9	21	while	while	SCONJ
hjic-103	9	22	maximizing	maximize	VERB
hjic-103	9	23	of	of	ADP
hjic-103	9	24	their	their	PRON
hjic-103	9	25	information	information	NOUN
hjic-103	9	26	content	content	NOUN
hjic-103	9	27	.	.	PUNCT
hjic-103	10	1	this	this	DET
hjic-103	10	2	paper	paper	NOUN
hjic-103	10	3	illustrates	illustrate	VERB
hjic-103	10	4	the	the	DET
hjic-103	10	5	applicability	applicability	NOUN
hjic-103	10	6	of	of	ADP
hjic-103	10	7	es	es	NOUN
hjic-103	10	8	for	for	ADP
hjic-103	10	9	the	the	DET
hjic-103	10	10	design	design	NOUN
hjic-103	10	11	of	of	ADP
hjic-103	10	12	feeding	feed	VERB
hjic-103	10	13	profile	profile	NOUN
hjic-103	10	14	for	for	ADP
hjic-103	10	15	a	a	DET
hjic-103	10	16	fed	feed	VERB
hjic-103	10	17	-	-	PUNCT
hjic-103	10	18	batch	batch	NOUN
hjic-103	10	19	biochemical	biochemical	ADJ
hjic-103	10	20	reactor	reactor	NOUN
hjic-103	10	21	.	.	PUNCT
hjic-103	11	1	the	the	DET
hjic-103	11	2	results	result	NOUN
hjic-103	11	3	illustrate	illustrate	VERB
hjic-103	11	4	that	that	SCONJ
hjic-103	11	5	if	if	SCONJ
hjic-103	11	6	the	the	DET
hjic-103	11	7	model	model	NOUN
hjic-103	11	8	structure	structure	NOUN
hjic-103	11	9	is	be	AUX
hjic-103	11	10	not	not	PART
hjic-103	11	11	accurate	accurate	ADJ
hjic-103	11	12	,	,	PUNCT
hjic-103	11	13	the	the	DET
hjic-103	11	14	evolutionary	evolutionary	ADJ
hjic-103	11	15	strategy	strategy	NOUN
hjic-103	11	16	can	can	AUX
hjic-103	11	17	result	result	VERB
hjic-103	11	18	in	in	ADP
hjic-103	11	19	more	more	ADJ
hjic-103	11	20	satisfactory	satisfactory	ADJ
hjic-103	11	21	parameter	parameter	NOUN
hjic-103	11	22	values	value	NOUN
hjic-103	11	23	than	than	ADP
hjic-103	11	24	the	the	DET
hjic-103	11	25	classical	classical	ADJ
hjic-103	11	26	sequential	sequential	ADJ
hjic-103	11	27	quadratic	quadratic	ADJ
hjic-103	11	28	programming	programming	NOUN
hjic-103	11	29	and	and	CCONJ
hjic-103	11	30	nonlinear	nonlinear	ADJ
hjic-103	11	31	least	least	ADJ
hjic-103	11	32	squares	square	NOUN
hjic-103	11	33	algorithms	algorithm	NOUN
hjic-103	11	34	.	.	PUNCT
hjic-103	12	1	keywords	keyword	NOUN
hjic-103	12	2	:	:	PUNCT
hjic-103	12	3	experiment	experiment	NOUN
hjic-103	12	4	design	design	NOUN
hjic-103	12	5	,	,	PUNCT
hjic-103	12	6	model	model	NOUN
hjic-103	12	7	identification	identification	NOUN
hjic-103	12	8	,	,	PUNCT
hjic-103	12	9	fed	feed	VERB
hjic-103	12	10	-	-	PUNCT
hjic-103	12	11	batch	batch	NOUN
hjic-103	12	12	bioreactor	bioreactor	NOUN
hjic-103	12	13	introduction	introduction	NOUN
hjic-103	12	14	process	process	NOUN
hjic-103	12	15	models	model	NOUN
hjic-103	12	16	play	play	VERB
hjic-103	12	17	important	important	ADJ
hjic-103	12	18	role	role	NOUN
hjic-103	12	19	in	in	ADP
hjic-103	12	20	computer	computer	NOUN
hjic-103	12	21	aided	aid	VERB
hjic-103	12	22	process	process	NOUN
hjic-103	12	23	engineering	engineering	NOUN
hjic-103	12	24	since	since	SCONJ
hjic-103	12	25	most	most	ADJ
hjic-103	12	26	of	of	ADP
hjic-103	12	27	advanced	advanced	ADJ
hjic-103	12	28	process	process	NOUN
hjic-103	12	29	monitoring	monitoring	NOUN
hjic-103	12	30	,	,	PUNCT
hjic-103	12	31	control	control	NOUN
hjic-103	12	32	,	,	PUNCT
hjic-103	12	33	and	and	CCONJ
hjic-103	12	34	optimization	optimization	NOUN
hjic-103	12	35	algorithms	algorithm	NOUN
hjic-103	12	36	relay	relay	NOUN
hjic-103	12	37	on	on	ADP
hjic-103	12	38	a	a	DET
hjic-103	12	39	model	model	NOUN
hjic-103	12	40	of	of	ADP
hjic-103	12	41	the	the	DET
hjic-103	12	42	process	process	NOUN
hjic-103	12	43	.	.	PUNCT
hjic-103	13	1	unfortunately	unfortunately	ADV
hjic-103	13	2	often	often	ADV
hjic-103	13	3	some	some	PRON
hjic-103	13	4	of	of	ADP
hjic-103	13	5	the	the	DET
hjic-103	13	6	parameters	parameter	NOUN
hjic-103	13	7	of	of	ADP
hjic-103	13	8	these	these	DET
hjic-103	13	9	models	model	NOUN
hjic-103	13	10	are	be	AUX
hjic-103	13	11	not	not	PART
hjic-103	13	12	known	know	VERB
hjic-103	13	13	a	a	DET
hjic-103	13	14	priori	priori	ADV
hjic-103	13	15	,	,	PUNCT
hjic-103	13	16	so	so	SCONJ
hjic-103	13	17	they	they	PRON
hjic-103	13	18	must	must	AUX
hjic-103	13	19	be	be	AUX
hjic-103	13	20	estimated	estimate	VERB
hjic-103	13	21	from	from	ADP
hjic-103	13	22	experimental	experimental	ADJ
hjic-103	13	23	data	datum	NOUN
hjic-103	13	24	.	.	PUNCT
hjic-103	14	1	the	the	DET
hjic-103	14	2	accuracy	accuracy	NOUN
hjic-103	14	3	of	of	ADP
hjic-103	14	4	these	these	DET
hjic-103	14	5	parameters	parameter	NOUN
hjic-103	14	6	largely	largely	ADV
hjic-103	14	7	depends	depend	VERB
hjic-103	14	8	on	on	ADP
hjic-103	14	9	the	the	DET
hjic-103	14	10	information	information	NOUN
hjic-103	14	11	content	content	NOUN
hjic-103	14	12	of	of	ADP
hjic-103	14	13	the	the	DET
hjic-103	14	14	experimental	experimental	ADJ
hjic-103	14	15	data	datum	NOUN
hjic-103	14	16	presented	present	VERB
hjic-103	14	17	to	to	ADP
hjic-103	14	18	the	the	DET
hjic-103	14	19	parameter	parameter	NOUN
hjic-103	14	20	identification	identification	NOUN
hjic-103	14	21	algorithm	algorithm	NOUN
hjic-103	14	22	[	[	X
hjic-103	14	23	1	1	NUM
hjic-103	14	24	]	]	PUNCT
hjic-103	14	25	.	.	PUNCT
hjic-103	15	1	establishing	establish	VERB
hjic-103	15	2	optimal	optimal	ADJ
hjic-103	15	3	experiment	experiment	NOUN
hjic-103	15	4	design	design	NOUN
hjic-103	15	5	(	(	PUNCT
hjic-103	15	6	oed	oed	PROPN
hjic-103	15	7	)	)	PUNCT
hjic-103	15	8	can	can	AUX
hjic-103	15	9	maximise	maximise	VERB
hjic-103	15	10	the	the	DET
hjic-103	15	11	confidence	confidence	NOUN
hjic-103	15	12	on	on	ADP
hjic-103	15	13	the	the	DET
hjic-103	15	14	parameters	parameter	NOUN
hjic-103	15	15	.	.	PUNCT
hjic-103	16	1	for	for	ADP
hjic-103	16	2	the	the	DET
hjic-103	16	3	identification	identification	NOUN
hjic-103	16	4	of	of	ADP
hjic-103	16	5	the	the	DET
hjic-103	16	6	parameters	parameter	NOUN
hjic-103	16	7	of	of	ADP
hjic-103	16	8	dynamic	dynamic	ADJ
hjic-103	16	9	models	model	NOUN
hjic-103	16	10	this	this	DET
hjic-103	16	11	approach	approach	NOUN
hjic-103	16	12	has	have	AUX
hjic-103	16	13	been	be	AUX
hjic-103	16	14	utilized	utilize	VERB
hjic-103	16	15	in	in	ADP
hjic-103	16	16	[	[	PUNCT
hjic-103	16	17	2	2	NUM
hjic-103	16	18	-	-	SYM
hjic-103	16	19	5	5	NUM
hjic-103	16	20	]	]	PUNCT
hjic-103	16	21	.	.	PUNCT
hjic-103	17	1	in	in	ADP
hjic-103	17	2	these	these	DET
hjic-103	17	3	studies	study	NOUN
hjic-103	17	4	experiment	experiment	NOUN
hjic-103	17	5	design	design	NOUN
hjic-103	17	6	is	be	AUX
hjic-103	17	7	concerned	concern	VERB
hjic-103	17	8	with	with	ADP
hjic-103	17	9	the	the	DET
hjic-103	17	10	following	follow	VERB
hjic-103	17	11	questions	question	NOUN
hjic-103	17	12	:	:	PUNCT
hjic-103	17	13	how	how	SCONJ
hjic-103	17	14	does	do	AUX
hjic-103	17	15	one	one	NUM
hjic-103	17	16	adjust	adjust	VERB
hjic-103	17	17	time	time	NOUN
hjic-103	17	18	-	-	PUNCT
hjic-103	17	19	varying	vary	VERB
hjic-103	17	20	controls	control	NOUN
hjic-103	17	21	,	,	PUNCT
hjic-103	17	22	initial	initial	ADJ
hjic-103	17	23	conditions	condition	NOUN
hjic-103	17	24	,	,	PUNCT
hjic-103	17	25	and/or	and/or	CCONJ
hjic-103	17	26	other	other	ADJ
hjic-103	17	27	design	design	NOUN
hjic-103	17	28	parameters	parameter	NOUN
hjic-103	17	29	of	of	ADP
hjic-103	17	30	the	the	DET
hjic-103	17	31	experiments	experiment	NOUN
hjic-103	17	32	to	to	PART
hjic-103	17	33	generate	generate	VERB
hjic-103	17	34	the	the	DET
hjic-103	17	35	maximum	maximum	ADJ
hjic-103	17	36	amount	amount	NOUN
hjic-103	17	37	of	of	ADP
hjic-103	17	38	information	information	NOUN
hjic-103	17	39	for	for	ADP
hjic-103	17	40	the	the	DET
hjic-103	17	41	purpose	purpose	NOUN
hjic-103	17	42	of	of	ADP
hjic-103	17	43	estimating	estimate	VERB
hjic-103	17	44	the	the	DET
hjic-103	17	45	parameters	parameter	NOUN
hjic-103	17	46	with	with	ADP
hjic-103	17	47	greatest	great	ADJ
hjic-103	17	48	precision	precision	NOUN
hjic-103	17	49	.	.	PUNCT
hjic-103	18	1	for	for	ADP
hjic-103	18	2	nonlinear	nonlinear	ADJ
hjic-103	18	3	models	model	NOUN
hjic-103	18	4	,	,	PUNCT
hjic-103	18	5	oed	oed	PROPN
hjic-103	18	6	is	be	AUX
hjic-103	18	7	based	base	VERB
hjic-103	18	8	on	on	ADP
hjic-103	18	9	an	an	DET
hjic-103	18	10	iterative	iterative	NOUN
hjic-103	18	11	algorithm	algorithm	NOUN
hjic-103	18	12	because	because	SCONJ
hjic-103	18	13	the	the	DET
hjic-103	18	14	optimal	optimal	ADJ
hjic-103	18	15	parameters	parameter	NOUN
hjic-103	18	16	of	of	ADP
hjic-103	18	17	the	the	DET
hjic-103	18	18	experiments	experiment	NOUN
hjic-103	18	19	depend	depend	VERB
hjic-103	18	20	on	on	ADP
hjic-103	18	21	the	the	DET
hjic-103	18	22	model	model	NOUN
hjic-103	18	23	parameters	parameter	NOUN
hjic-103	18	24	that	that	PRON
hjic-103	18	25	are	be	AUX
hjic-103	18	26	going	go	VERB
hjic-103	18	27	to	to	PART
hjic-103	18	28	be	be	AUX
hjic-103	18	29	estimated	estimate	VERB
hjic-103	18	30	based	base	VERB
hjic-103	18	31	on	on	ADP
hjic-103	18	32	the	the	DET
hjic-103	18	33	result	result	NOUN
hjic-103	18	34	of	of	ADP
hjic-103	18	35	the	the	DET
hjic-103	18	36	designed	design	VERB
hjic-103	18	37	experiment	experiment	NOUN
hjic-103	18	38	.	.	PUNCT
hjic-103	19	1	consequently	consequently	ADV
hjic-103	19	2	,	,	PUNCT
hjic-103	19	3	oed	oed	PROPN
hjic-103	19	4	estimates	estimate	VERB
hjic-103	19	5	the	the	DET
hjic-103	19	6	model	model	NOUN
hjic-103	19	7	parameters	parameter	NOUN
hjic-103	19	8	and	and	CCONJ
hjic-103	19	9	designs	design	VERB
hjic-103	19	10	the	the	DET
hjic-103	19	11	experiment	experiment	NOUN
hjic-103	19	12	iteratively	iteratively	ADV
hjic-103	19	13	.	.	PUNCT
hjic-103	20	1	both	both	DET
hjic-103	20	2	parameter	parameter	NOUN
hjic-103	20	3	estimation	estimation	NOUN
hjic-103	20	4	and	and	CCONJ
hjic-103	20	5	experiment	experiment	NOUN
hjic-103	20	6	design	design	NOUN
hjic-103	20	7	are	be	AUX
hjic-103	20	8	based	base	VERB
hjic-103	20	9	on	on	ADP
hjic-103	20	10	nonlinear	nonlinear	ADJ
hjic-103	20	11	optimization	optimization	NOUN
hjic-103	20	12	of	of	ADP
hjic-103	20	13	certain	certain	ADJ
hjic-103	20	14	cost	cost	NOUN
hjic-103	20	15	-	-	PUNCT
hjic-103	20	16	functions	function	NOUN
hjic-103	20	17	.	.	PUNCT
hjic-103	21	1	in	in	ADP
hjic-103	21	2	practice	practice	NOUN
hjic-103	21	3	,	,	PUNCT
hjic-103	21	4	the	the	DET
hjic-103	21	5	applied	apply	VERB
hjic-103	21	6	nonlinear	nonlinear	ADJ
hjic-103	21	7	optimization	optimization	NOUN
hjic-103	21	8	algorithms	algorithm	NOUN
hjic-103	21	9	have	have	VERB
hjic-103	21	10	great	great	ADJ
hjic-103	21	11	influence	influence	NOUN
hjic-103	21	12	on	on	ADP
hjic-103	21	13	the	the	DET
hjic-103	21	14	whole	whole	ADJ
hjic-103	21	15	procedure	procedure	NOUN
hjic-103	21	16	,	,	PUNCT
hjic-103	21	17	because	because	SCONJ
hjic-103	21	18	for	for	ADP
hjic-103	21	19	nonlinear	nonlinear	ADJ
hjic-103	21	20	dynamical	dynamical	ADJ
hjic-103	21	21	models	model	NOUN
hjic-103	21	22	the	the	DET
hjic-103	21	23	design	design	NOUN
hjic-103	21	24	of	of	ADP
hjic-103	21	25	the	the	DET
hjic-103	21	26	experiment	experiment	NOUN
hjic-103	21	27	is	be	AUX
hjic-103	21	28	a	a	DET
hjic-103	21	29	hard	hard	ADJ
hjic-103	21	30	optimization	optimization	NOUN
hjic-103	21	31	problem	problem	NOUN
hjic-103	21	32	.	.	PUNCT
hjic-103	22	1	as	as	ADP
hjic-103	22	2	an	an	DET
hjic-103	22	3	effective	effective	ADJ
hjic-103	22	4	optimization	optimization	NOUN
hjic-103	22	5	algorithm	algorithm	NOUN
hjic-103	22	6	,	,	PUNCT
hjic-103	22	7	this	this	DET
hjic-103	22	8	paper	paper	NOUN
hjic-103	22	9	proposes	propose	VERB
hjic-103	22	10	the	the	DET
hjic-103	22	11	application	application	NOUN
hjic-103	22	12	of	of	ADP
hjic-103	22	13	evolutionary	evolutionary	ADJ
hjic-103	22	14	strategy	strategy	NOUN
hjic-103	22	15	(	(	PUNCT
hjic-103	22	16	es	es	NOUN
hjic-103	22	17	)	)	PUNCT
hjic-103	22	18	for	for	ADP
hjic-103	22	19	this	this	DET
hjic-103	22	20	purpose	purpose	NOUN
hjic-103	22	21	.	.	PUNCT
hjic-103	23	1	es	es	X
hjic-103	23	2	is	be	AUX
hjic-103	23	3	a	a	DET
hjic-103	23	4	stochastic	stochastic	ADJ
hjic-103	23	5	optimization	optimization	NOUN
hjic-103	23	6	algorithm	algorithm	NOUN
hjic-103	23	7	that	that	PRON
hjic-103	23	8	uses	use	VERB
hjic-103	23	9	the	the	DET
hjic-103	23	10	model	model	NOUN
hjic-103	23	11	of	of	ADP
hjic-103	23	12	natural	natural	ADJ
hjic-103	23	13	selection	selection	NOUN
hjic-103	23	14	[	[	X
hjic-103	23	15	7	7	NUM
hjic-103	23	16	]	]	PUNCT
hjic-103	23	17	.	.	PUNCT
hjic-103	24	1	in	in	ADP
hjic-103	24	2	this	this	DET
hjic-103	24	3	paper	paper	NOUN
hjic-103	24	4	,	,	PUNCT
hjic-103	24	5	oed	oed	PROPN
hjic-103	24	6	are	be	AUX
hjic-103	24	7	applied	apply	VERB
hjic-103	24	8	for	for	ADP
hjic-103	24	9	fed	fed	NOUN
hjic-103	24	10	-	-	PUNCT
hjic-103	24	11	batch	batch	NOUN
hjic-103	24	12	biochemical	biochemical	ADJ
hjic-103	24	13	reactor	reactor	NOUN
hjic-103	24	14	.	.	PUNCT
hjic-103	25	1	one	one	NUM
hjic-103	25	2	of	of	ADP
hjic-103	25	3	the	the	DET
hjic-103	25	4	factors	factor	NOUN
hjic-103	25	5	affecting	affect	VERB
hjic-103	25	6	the	the	DET
hjic-103	25	7	modelling	modelling	NOUN
hjic-103	25	8	of	of	ADP
hjic-103	25	9	biochemical	biochemical	ADJ
hjic-103	25	10	systems	system	NOUN
hjic-103	25	11	is	be	AUX
hjic-103	25	12	that	that	DET
hjic-103	25	13	accurate	accurate	ADJ
hjic-103	25	14	description	description	NOUN
hjic-103	25	15	of	of	ADP
hjic-103	25	16	biochemical	biochemical	ADJ
hjic-103	25	17	reaction	reaction	NOUN
hjic-103	25	18	is	be	AUX
hjic-103	25	19	generally	generally	ADV
hjic-103	25	20	not	not	PART
hjic-103	25	21	available	available	ADJ
hjic-103	25	22	a	a	DET
hjic-103	25	23	priori	priori	ADV
hjic-103	25	24	.	.	PUNCT
hjic-103	26	1	hence	hence	ADV
hjic-103	26	2	usually	usually	ADV
hjic-103	26	3	a	a	DET
hjic-103	26	4	simplified	simplified	ADJ
hjic-103	26	5	kinetic	kinetic	ADJ
hjic-103	26	6	model	model	NOUN
hjic-103	26	7	,	,	PUNCT
hjic-103	26	8	e.g.	e.g.	ADV
hjic-103	26	9	monod	monod	NOUN
hjic-103	26	10	model	model	NOUN
hjic-103	26	11	,	,	PUNCT
hjic-103	26	12	is	be	AUX
hjic-103	26	13	used	use	VERB
hjic-103	26	14	to	to	PART
hjic-103	26	15	describe	describe	VERB
hjic-103	26	16	the	the	DET
hjic-103	26	17	microbial	microbial	ADJ
hjic-103	26	18	dynamics	dynamic	NOUN
hjic-103	26	19	.	.	PUNCT
hjic-103	27	1	some	some	DET
hjic-103	27	2	results	result	NOUN
hjic-103	27	3	were	be	AUX
hjic-103	27	4	presented	present	VERB
hjic-103	27	5	for	for	ADP
hjic-103	27	6	experiment	experiment	NOUN
hjic-103	27	7	design	design	NOUN
hjic-103	27	8	of	of	ADP
hjic-103	27	9	biochemical	biochemical	ADJ
hjic-103	27	10	systems	system	NOUN
hjic-103	27	11	in	in	ADP
hjic-103	27	12	[	[	PUNCT
hjic-103	27	13	4	4	NUM
hjic-103	27	14	-	-	SYM
hjic-103	27	15	6	6	NUM
hjic-103	27	16	]	]	PUNCT
hjic-103	27	17	,	,	PUNCT
hjic-103	27	18	but	but	CCONJ
hjic-103	27	19	these	these	DET
hjic-103	27	20	works	work	NOUN
hjic-103	27	21	assumes	assume	VERB
hjic-103	27	22	that	that	SCONJ
hjic-103	27	23	the	the	DET
hjic-103	27	24	model	model	NOUN
hjic-103	27	25	structure	structure	NOUN
hjic-103	27	26	is	be	AUX
hjic-103	27	27	perfectly	perfectly	ADV
hjic-103	27	28	known	know	VERB
hjic-103	27	29	.	.	PUNCT
hjic-103	28	1	this	this	DET
hjic-103	28	2	paper	paper	NOUN
hjic-103	28	3	discusses	discuss	VERB
hjic-103	28	4	the	the	DET
hjic-103	28	5	application	application	NOUN
hjic-103	28	6	of	of	ADP
hjic-103	28	7	oed	oed	PROPN
hjic-103	28	8	based	base	VERB
hjic-103	28	9	on	on	ADP
hjic-103	28	10	models	model	NOUN
hjic-103	28	11	that	that	PRON
hjic-103	28	12	have	have	VERB
hjic-103	28	13	structural	structural	ADJ
hjic-103	28	14	uncertainty	uncertainty	NOUN
hjic-103	28	15	.	.	PUNCT
hjic-103	29	1	our	our	PRON
hjic-103	29	2	results	result	NOUN
hjic-103	29	3	illustrate	illustrate	VERB
hjic-103	29	4	that	that	SCONJ
hjic-103	29	5	although	although	SCONJ
hjic-103	29	6	the	the	DET
hjic-103	29	7	model	model	NOUN
hjic-103	29	8	structure	structure	NOUN
hjic-103	29	9	used	use	VERB
hjic-103	29	10	for	for	ADP
hjic-103	29	11	the	the	DET
hjic-103	29	12	design	design	NOUN
hjic-103	29	13	of	of	ADP
hjic-103	29	14	the	the	DET
hjic-103	29	15	experiments	experiment	NOUN
hjic-103	29	16	is	be	AUX
hjic-103	29	17	not	not	PART
hjic-103	29	18	accurate	accurate	ADJ
hjic-103	29	19	,	,	PUNCT
hjic-103	29	20	oed	oe	VERB
hjic-103	29	21	with	with	ADP
hjic-103	29	22	es	es	PRON
hjic-103	29	23	can	can	AUX
hjic-103	29	24	result	result	VERB
hjic-103	29	25	in	in	ADP
hjic-103	29	26	satisfactory	satisfactory	ADJ
hjic-103	29	27	parameter	parameter	NOUN
hjic-103	29	28	values	value	NOUN
hjic-103	29	29	.	.	PUNCT
hjic-103	30	1	the	the	DET
hjic-103	30	2	paper	paper	NOUN
hjic-103	30	3	organized	organize	VERB
hjic-103	30	4	as	as	SCONJ
hjic-103	30	5	follows	follow	VERB
hjic-103	30	6	:	:	PUNCT
hjic-103	30	7	the	the	DET
hjic-103	30	8	first	first	ADJ
hjic-103	30	9	section	section	NOUN
hjic-103	30	10	reviews	review	VERB
hjic-103	30	11	the	the	DET
hjic-103	30	12	theory	theory	NOUN
hjic-103	30	13	of	of	ADP
hjic-103	30	14	optimal	optimal	ADJ
hjic-103	30	15	experiment	experiment	NOUN
hjic-103	30	16	design	design	NOUN
hjic-103	30	17	.	.	PUNCT
hjic-103	31	1	the	the	DET
hjic-103	31	2	second	second	ADJ
hjic-103	31	3	section	section	NOUN
hjic-103	31	4	proposes	propose	VERB
hjic-103	31	5	the	the	DET
hjic-103	31	6	application	application	NOUN
hjic-103	31	7	of	of	ADP
hjic-103	31	8	evolutionary	evolutionary	ADJ
hjic-103	31	9	strategy	strategy	NOUN
hjic-103	31	10	for	for	ADP
hjic-103	31	11	oed	oed	PROPN
hjic-103	31	12	.	.	PUNCT
hjic-103	32	1	the	the	DET
hjic-103	32	2	third	third	ADJ
hjic-103	32	3	section	section	NOUN
hjic-103	32	4	presents	present	VERB
hjic-103	32	5	the	the	DET
hjic-103	32	6	application	application	NOUN
hjic-103	32	7	example	example	NOUN
hjic-103	32	8	.	.	PUNCT
hjic-103	33	1	finally	finally	ADV
hjic-103	33	2	,	,	PUNCT
hjic-103	33	3	conclusions	conclusion	NOUN
hjic-103	33	4	are	be	AUX
hjic-103	33	5	given	give	VERB
hjic-103	33	6	in	in	ADP
hjic-103	33	7	the	the	DET
hjic-103	33	8	fourth	fourth	ADJ
hjic-103	33	9	section	section	NOUN
hjic-103	33	10	.	.	PUNCT
hjic-103	34	1	correspondence	correspondence	NOUN
hjic-103	34	2	concerning	concern	VERB
hjic-103	34	3	this	this	DET
hjic-103	34	4	article	article	NOUN
hjic-103	34	5	should	should	AUX
hjic-103	34	6	be	be	AUX
hjic-103	34	7	addressed	address	VERB
hjic-103	34	8	to	to	ADP
hjic-103	34	9	j.	j.	PROPN
hjic-103	34	10	abonyi	abonyi	PROPN
hjic-103	34	11	(	(	PUNCT
hjic-103	34	12	abonyij@fmt.vein.hu	abonyij@fmt.vein.hu	PROPN
hjic-103	34	13	)	)	PUNCT
hjic-103	34	14	114	114	NUM
hjic-103	34	15	optimal	optimal	ADJ
hjic-103	34	16	input	input	NOUN
hjic-103	34	17	design	design	NOUN
hjic-103	34	18	for	for	ADP
hjic-103	34	19	parameter	parameter	NOUN
hjic-103	34	20	estimation	estimation	NOUN
hjic-103	34	21	the	the	DET
hjic-103	34	22	case	case	NOUN
hjic-103	34	23	study	study	NOUN
hjic-103	34	24	considered	consider	VERB
hjic-103	34	25	in	in	ADP
hjic-103	34	26	this	this	DET
hjic-103	34	27	paper	paper	NOUN
hjic-103	34	28	belongs	belong	VERB
hjic-103	34	29	to	to	ADP
hjic-103	34	30	the	the	DET
hjic-103	34	31	following	follow	VERB
hjic-103	34	32	general	general	ADJ
hjic-103	34	33	class	class	NOUN
hjic-103	34	34	of	of	ADP
hjic-103	34	35	process	process	NOUN
hjic-103	34	36	models	model	NOUN
hjic-103	34	37	:	:	PUNCT
hjic-103	34	38	(	(	PUNCT
hjic-103	34	39	)	)	PUNCT
hjic-103	34	40	(	(	PUNCT
hjic-103	34	41	)	)	PUNCT
hjic-103	34	42	(	(	PUNCT
hjic-103	34	43	)	)	PUNCT
hjic-103	34	44	(	(	PUNCT
hjic-103	34	45	)	)	PUNCT
hjic-103	34	46	(	(	PUNCT
hjic-103	34	47	1	1	X
hjic-103	34	48	)	)	PUNCT
hjic-103	34	49	(	(	PUNCT
hjic-103	34	50	)	)	PUNCT
hjic-103	34	51	(	(	PUNCT
hjic-103	34	52	)	)	PUNCT
hjic-103	34	53	(	(	PUNCT
hjic-103	34	54	)	)	PUNCT
hjic-103	34	55	tt	tt	PROPN
hjic-103	34	56	t	t	PROPN
hjic-103	34	57	,	,	PUNCT
hjic-103	34	58	ut	ut	PROPN
hjic-103	34	59	t	t	PROPN
hjic-103	34	60	t	t	PROPN
hjic-103	35	1	xgy	xgy	PROPN
hjic-103	35	2	pxfx	pxfx	NOUN
hjic-103	35	3	=	=	PROPN
hjic-103	36	1	=	=	PUNCT
hjic-103	36	2	,	,	PUNCT
hjic-103	36	3	d	d	PROPN
hjic-103	36	4	d	d	X
hjic-103	36	5	where	where	SCONJ
hjic-103	36	6	u	u	NOUN
hjic-103	36	7	is	be	AUX
hjic-103	36	8	the	the	DET
hjic-103	36	9	manipulated	manipulate	VERB
hjic-103	36	10	input	input	NOUN
hjic-103	36	11	,	,	PUNCT
hjic-103	36	12	y	y	PROPN
hjic-103	36	13	is	be	AUX
hjic-103	36	14	the	the	DET
hjic-103	36	15	output	output	NOUN
hjic-103	36	16	(	(	PUNCT
hjic-103	36	17	vector	vector	NOUN
hjic-103	36	18	)	)	PUNCT
hjic-103	36	19	,	,	PUNCT
hjic-103	36	20	x	x	X
hjic-103	36	21	is	be	AUX
hjic-103	36	22	the	the	DET
hjic-103	36	23	state	state	NOUN
hjic-103	36	24	(	(	PUNCT
hjic-103	36	25	vector	vector	NOUN
hjic-103	36	26	)	)	PUNCT
hjic-103	36	27	of	of	ADP
hjic-103	36	28	the	the	DET
hjic-103	36	29	system	system	NOUN
hjic-103	36	30	and	and	CCONJ
hjic-103	36	31	p	p	NOUN
hjic-103	36	32	denotes	denote	VERB
hjic-103	36	33	the	the	DET
hjic-103	36	34	unknown	unknown	ADJ
hjic-103	36	35	model	model	NOUN
hjic-103	36	36	parameters	parameter	NOUN
hjic-103	36	37	.	.	PUNCT
hjic-103	37	1	the	the	DET
hjic-103	37	2	p	p	PROPN
hjic-103	37	3	parameters	parameter	NOUN
hjic-103	37	4	are	be	AUX
hjic-103	37	5	unknown	unknown	ADJ
hjic-103	37	6	and	and	CCONJ
hjic-103	37	7	should	should	AUX
hjic-103	37	8	be	be	AUX
hjic-103	37	9	estimated	estimate	VERB
hjic-103	37	10	based	base	VERB
hjic-103	37	11	on	on	ADP
hjic-103	37	12	data	datum	NOUN
hjic-103	37	13	taken	take	VERB
hjic-103	37	14	from	from	ADP
hjic-103	37	15	experiments	experiment	NOUN
hjic-103	37	16	.	.	PUNCT
hjic-103	38	1	for	for	ADP
hjic-103	38	2	the	the	DET
hjic-103	38	3	estimation	estimation	NOUN
hjic-103	38	4	of	of	ADP
hjic-103	38	5	these	these	DET
hjic-103	38	6	parameters	parameter	NOUN
hjic-103	38	7	classical	classical	ADJ
hjic-103	38	8	parameter	parameter	NOUN
hjic-103	38	9	identification	identification	NOUN
hjic-103	38	10	approach	approach	NOUN
hjic-103	38	11	is	be	AUX
hjic-103	38	12	used	use	VERB
hjic-103	38	13	that	that	PRON
hjic-103	38	14	is	be	AUX
hjic-103	38	15	based	base	VERB
hjic-103	38	16	on	on	ADP
hjic-103	38	17	the	the	DET
hjic-103	38	18	minimization	minimization	NOUN
hjic-103	38	19	of	of	ADP
hjic-103	38	20	the	the	DET
hjic-103	38	21	square	square	ADJ
hjic-103	38	22	error	error	NOUN
hjic-103	38	23	between	between	ADP
hjic-103	38	24	the	the	DET
hjic-103	38	25	output	output	NOUN
hjic-103	38	26	of	of	ADP
hjic-103	38	27	the	the	DET
hjic-103	38	28	system	system	NOUN
hjic-103	38	29	and	and	CCONJ
hjic-103	38	30	the	the	DET
hjic-103	38	31	output	output	NOUN
hjic-103	38	32	of	of	ADP
hjic-103	38	33	the	the	DET
hjic-103	38	34	model	model	NOUN
hjic-103	38	35	:	:	PUNCT
hjic-103	38	36	(	(	PUNCT
hjic-103	38	37	)	)	PUNCT
hjic-103	38	38	(	(	PUNCT
hjic-103	38	39	)	)	PUNCT
hjic-103	38	40	p	p	X
hjic-103	38	41	p	p	PROPN
hjic-103	38	42	,	,	PUNCT
hjic-103	38	43	min	min	PROPN
hjic-103	38	44	tuj	tuj	PROPN
hjic-103	38	45	mse	mse	PROPN
hjic-103	38	46	(	(	PUNCT
hjic-103	38	47	2	2	NUM
hjic-103	38	48	)	)	PUNCT
hjic-103	38	49	where	where	SCONJ
hjic-103	38	50	(	(	PUNCT
hjic-103	38	51	)	)	PUNCT
hjic-103	38	52	(	(	PUNCT
hjic-103	38	53	)	)	PUNCT
hjic-103	38	54	(	(	PUNCT
hjic-103	38	55	)	)	PUNCT
hjic-103	38	56	(	(	PUNCT
hjic-103	38	57	)	)	PUNCT
hjic-103	38	58	(	(	PUNCT
hjic-103	38	59	)	)	PUNCT
hjic-103	38	60	(	(	PUNCT
hjic-103	38	61	(	(	PUNCT
hjic-103	38	62	)	)	PUNCT
hjic-103	38	63	(	(	PUNCT
hjic-103	38	64	)	)	PUNCT
hjic-103	38	65	(	(	PUNCT
hjic-103	38	66	)	)	PUNCT
hjic-103	38	67	(	(	PUNCT
hjic-103	38	68	)	)	PUNCT
hjic-103	38	69	(	(	PUNCT
hjic-103	38	70	)	)	PUNCT
hjic-103	38	71	pyye	pyye	NOUN
hjic-103	38	72	eqep	eqep	VERB
hjic-103	38	73	,	,	PUNCT
hjic-103	38	74	~	~	PUNCT
hjic-103	38	75	d1	d1	NOUN
hjic-103	38	76	,	,	PUNCT
hjic-103	38	77	0	0	NUM
hjic-103	38	78	t	t	NOUN
hjic-103	38	79	tutut	tutut	VERB
hjic-103	38	80	tttt	tttt	PROPN
hjic-103	38	81	t	t	PROPN
hjic-103	38	82	tuj	tuj	PROPN
hjic-103	38	83	ft	ft	PROPN
hjic-103	38	84	tf	tf	PROPN
hjic-103	38	85	mse	mse	PROPN
hjic-103	38	86	−=	−=	PROPN
hjic-103	38	87	⋅⋅=	⋅⋅=	PROPN
hjic-103	38	88	∫	∫	PROPN
hjic-103	38	89	=	=	SYM
hjic-103	38	90	)	)	PUNCT
hjic-103	38	91	(	(	PUNCT
hjic-103	38	92	3	3	X
hjic-103	38	93	)	)	PUNCT
hjic-103	38	94	in	in	ADP
hjic-103	38	95	which	which	PRON
hjic-103	38	96	is	be	AUX
hjic-103	38	97	the	the	DET
hjic-103	38	98	output	output	NOUN
hjic-103	38	99	of	of	ADP
hjic-103	38	100	the	the	DET
hjic-103	38	101	system	system	NOUN
hjic-103	38	102	for	for	ADP
hjic-103	38	103	a	a	DET
hjic-103	38	104	certain	certain	ADJ
hjic-103	38	105	u(t	u(t	NOUN
hjic-103	38	106	)	)	PUNCT
hjic-103	38	107	input	input	NOUN
hjic-103	38	108	profile	profile	NOUN
hjic-103	38	109	,	,	PUNCT
hjic-103	38	110	and	and	CCONJ
hjic-103	38	111	y	y	PROPN
hjic-103	38	112	is	be	AUX
hjic-103	38	113	the	the	DET
hjic-103	38	114	output	output	NOUN
hjic-103	38	115	of	of	ADP
hjic-103	38	116	the	the	DET
hjic-103	38	117	model	model	NOUN
hjic-103	38	118	for	for	ADP
hjic-103	38	119	the	the	DET
hjic-103	38	120	same	same	ADJ
hjic-103	38	121	u(t	u(t	NOUN
hjic-103	38	122	)	)	PUNCT
hjic-103	38	123	input	input	NOUN
hjic-103	38	124	profile	profile	NOUN
hjic-103	38	125	with	with	ADP
hjic-103	38	126	p	p	NOUN
hjic-103	38	127	parameters	parameter	NOUN
hjic-103	38	128	,	,	PUNCT
hjic-103	38	129	q	q	PUNCT
hjic-103	38	130	is	be	AUX
hjic-103	38	131	a	a	DET
hjic-103	38	132	user	user	NOUN
hjic-103	38	133	supplied	supply	VERB
hjic-103	38	134	square	square	ADJ
hjic-103	38	135	weighting	weighting	NOUN
hjic-103	38	136	matrix	matrix	NOUN
hjic-103	38	137	that	that	PRON
hjic-103	38	138	represents	represent	VERB
hjic-103	38	139	the	the	DET
hjic-103	38	140	(	(	PUNCT
hjic-103	38	141	variance	variance	NOUN
hjic-103	38	142	of	of	ADP
hjic-103	38	143	the	the	DET
hjic-103	38	144	)	)	PUNCT
hjic-103	38	145	measurement	measurement	NOUN
hjic-103	38	146	error	error	NOUN
hjic-103	38	147	.	.	PUNCT
hjic-103	39	1	y~	y~	PROPN
hjic-103	39	2	the	the	DET
hjic-103	39	3	accuracy	accuracy	NOUN
hjic-103	39	4	of	of	ADP
hjic-103	39	5	the	the	DET
hjic-103	39	6	parameter	parameter	NOUN
hjic-103	39	7	estimation	estimation	NOUN
hjic-103	39	8	depends	depend	VERB
hjic-103	39	9	on	on	ADP
hjic-103	39	10	the	the	DET
hjic-103	39	11	applied	apply	VERB
hjic-103	39	12	u(t	u(t	NOUN
hjic-103	39	13	)	)	PUNCT
hjic-103	39	14	input	input	NOUN
hjic-103	39	15	profile	profile	NOUN
hjic-103	39	16	.	.	PUNCT
hjic-103	40	1	the	the	DET
hjic-103	40	2	goal	goal	NOUN
hjic-103	40	3	of	of	ADP
hjic-103	40	4	the	the	DET
hjic-103	40	5	experiment	experiment	NOUN
hjic-103	40	6	design	design	NOUN
hjic-103	40	7	is	be	AUX
hjic-103	40	8	to	to	PART
hjic-103	40	9	determine	determine	VERB
hjic-103	40	10	an	an	DET
hjic-103	40	11	optimal	optimal	ADJ
hjic-103	40	12	input	input	NOUN
hjic-103	40	13	profile	profile	NOUN
hjic-103	40	14	in	in	ADP
hjic-103	40	15	the	the	DET
hjic-103	40	16	sense	sense	NOUN
hjic-103	40	17	of	of	ADP
hjic-103	40	18	the	the	DET
hjic-103	40	19	parameter	parameter	NOUN
hjic-103	40	20	estimation	estimation	NOUN
hjic-103	40	21	leads	lead	VERB
hjic-103	40	22	to	to	ADP
hjic-103	40	23	optimal	optimal	ADJ
hjic-103	40	24	parameters	parameter	NOUN
hjic-103	40	25	with	with	ADP
hjic-103	40	26	maximal	maximal	ADJ
hjic-103	40	27	confidence	confidence	NOUN
hjic-103	40	28	.	.	PUNCT
hjic-103	41	1	the	the	DET
hjic-103	41	2	basic	basic	ADJ
hjic-103	41	3	element	element	NOUN
hjic-103	41	4	of	of	ADP
hjic-103	41	5	the	the	DET
hjic-103	41	6	experiment	experiment	NOUN
hjic-103	41	7	design	design	NOUN
hjic-103	41	8	methodology	methodology	NOUN
hjic-103	41	9	is	be	AUX
hjic-103	41	10	the	the	DET
hjic-103	41	11	fisher	fisher	PROPN
hjic-103	41	12	information	information	NOUN
hjic-103	41	13	matrix	matrix	NOUN
hjic-103	41	14	f	f	X
hjic-103	41	15	,	,	PUNCT
hjic-103	41	16	which	which	PRON
hjic-103	41	17	combines	combine	VERB
hjic-103	41	18	information	information	NOUN
hjic-103	41	19	on	on	ADP
hjic-103	41	20	(	(	PUNCT
hjic-103	41	21	i	i	NOUN
hjic-103	41	22	)	)	PUNCT
hjic-103	41	23	the	the	DET
hjic-103	41	24	output	output	NOUN
hjic-103	41	25	measurement	measurement	NOUN
hjic-103	41	26	error	error	NOUN
hjic-103	41	27	and	and	CCONJ
hjic-103	41	28	(	(	PUNCT
hjic-103	41	29	ii	ii	NOUN
hjic-103	41	30	)	)	PUNCT
hjic-103	41	31	the	the	DET
hjic-103	41	32	sensitivity	sensitivity	NOUN
hjic-103	41	33	of	of	ADP
hjic-103	41	34	the	the	DET
hjic-103	41	35	model	model	NOUN
hjic-103	41	36	output	output	NOUN
hjic-103	41	37	with	with	ADP
hjic-103	41	38	respect	respect	NOUN
hjic-103	41	39	to	to	ADP
hjic-103	41	40	the	the	DET
hjic-103	41	41	model	model	NOUN
hjic-103	41	42	parameters	parameter	NOUN
hjic-103	41	43	:	:	PUNCT
hjic-103	41	44	(	(	PUNCT
hjic-103	41	45	)	)	PUNCT
hjic-103	41	46	(	(	PUNCT
hjic-103	41	47	)	)	PUNCT
hjic-103	41	48	(	(	PUNCT
hjic-103	41	49	)	)	PUNCT
hjic-103	41	50	(	(	PUNCT
hjic-103	41	51	)	)	PUNCT
hjic-103	41	52	∫	∫	PROPN
hjic-103	42	1	=	=	SYM
hjic-103	43	1	=	=	PUNCT
hjic-103	43	2	=	=	SYM
hjic-103	43	3	⎟⎟	⎟⎟	ADJ
hjic-103	43	4	⎠	⎠	NOUN
hjic-103	43	5	⎞	⎞	NOUN
hjic-103	43	6	⎜⎜	⎜⎜	NOUN
hjic-103	43	7	⎝	⎝	PUNCT
hjic-103	43	8	⎛	⎛	NOUN
hjic-103	43	9	∂	∂	NUM
hjic-103	43	10	∂	∂	NUM
hjic-103	43	11	⋅⋅⎟⎟	⋅⋅⎟⎟	NOUN
hjic-103	43	12	⎠	⎠	NOUN
hjic-103	43	13	⎞	⎞	PROPN
hjic-103	43	14	⎜⎜	⎜⎜	PROPN
hjic-103	43	15	⎝	⎝	PUNCT
hjic-103	43	16	⎛	⎛	NOUN
hjic-103	43	17	∂	∂	NUM
hjic-103	43	18	∂	∂	NOUN
hjic-103	43	19	=	=	SYM
hjic-103	43	20	ft	ft	PROPN
hjic-103	43	21	t	t	PROPN
hjic-103	43	22	pppp	pppp	PROPN
hjic-103	43	23	f	f	PROPN
hjic-103	43	24	tttt	tttt	PROPN
hjic-103	43	25	t	t	PROPN
hjic-103	43	26	0	0	NUM
hjic-103	43	27	t	t	PROPN
hjic-103	43	28	0	0	NUM
hjic-103	43	29	d,,1	d,,1	NOUN
hjic-103	43	30	00	00	PUNCT
hjic-103	44	1	p	p	PROPN
hjic-103	45	1	p	p	X
hjic-103	46	1	yqp	yqp	INTJ
hjic-103	47	1	p	p	PROPN
hjic-103	47	2	ypf	ypf	PROPN
hjic-103	47	3	(	(	PUNCT
hjic-103	47	4	4	4	NUM
hjic-103	47	5	)	)	PUNCT
hjic-103	47	6	in	in	ADP
hjic-103	47	7	which	which	PRON
hjic-103	47	8	p0	p0	NOUN
hjic-103	47	9	is	be	AUX
hjic-103	47	10	the	the	DET
hjic-103	47	11	nominal	nominal	ADJ
hjic-103	47	12	parameter	parameter	NOUN
hjic-103	47	13	vector	vector	NOUN
hjic-103	47	14	.	.	PUNCT
hjic-103	48	1	the	the	DET
hjic-103	48	2	fisher	fisher	PROPN
hjic-103	48	3	information	information	NOUN
hjic-103	48	4	matrix	matrix	NOUN
hjic-103	48	5	f	f	PRON
hjic-103	48	6	provides	provide	VERB
hjic-103	48	7	an	an	DET
hjic-103	48	8	approximate	approximate	ADJ
hjic-103	48	9	quantification	quantification	NOUN
hjic-103	48	10	of	of	ADP
hjic-103	48	11	the	the	DET
hjic-103	48	12	attainable	attainable	ADJ
hjic-103	48	13	parameter	parameter	NOUN
hjic-103	48	14	estimation	estimation	NOUN
hjic-103	48	15	quality	quality	NOUN
hjic-103	48	16	in	in	ADP
hjic-103	48	17	the	the	DET
hjic-103	48	18	neighbourhood	neighbourhood	NOUN
hjic-103	48	19	of	of	ADP
hjic-103	48	20	the	the	DET
hjic-103	48	21	nominal	nominal	ADJ
hjic-103	48	22	parameter	parameter	NOUN
hjic-103	48	23	vector	vector	NOUN
hjic-103	48	24	p0	p0	NOUN
hjic-103	48	25	for	for	ADP
hjic-103	48	26	a	a	DET
hjic-103	48	27	particular	particular	ADJ
hjic-103	48	28	experiment	experiment	NOUN
hjic-103	48	29	as	as	ADP
hjic-103	48	30	the	the	DET
hjic-103	48	31	inverse	inverse	NOUN
hjic-103	48	32	of	of	ADP
hjic-103	48	33	matrix	matrix	NOUN
hjic-103	48	34	f	f	PROPN
hjic-103	48	35	approximates	approximate	VERB
hjic-103	48	36	the	the	DET
hjic-103	48	37	parameter	parameter	NOUN
hjic-103	48	38	estimation	estimation	NOUN
hjic-103	48	39	covariance	covariance	NOUN
hjic-103	48	40	matrix	matrix	NOUN
hjic-103	48	41	.	.	PUNCT
hjic-103	49	1	the	the	DET
hjic-103	49	2	optimal	optimal	ADJ
hjic-103	49	3	design	design	NOUN
hjic-103	49	4	criterion	criterion	NOUN
hjic-103	49	5	aims	aim	VERB
hjic-103	49	6	the	the	DET
hjic-103	49	7	minimization	minimization	NOUN
hjic-103	49	8	of	of	ADP
hjic-103	49	9	a	a	DET
hjic-103	49	10	scalar	scalar	ADJ
hjic-103	49	11	function	function	NOUN
hjic-103	49	12	of	of	ADP
hjic-103	49	13	the	the	DET
hjic-103	49	14	f	f	PROPN
hjic-103	49	15	matrix	matrix	NOUN
hjic-103	49	16	.	.	PUNCT
hjic-103	50	1	among	among	ADP
hjic-103	50	2	the	the	DET
hjic-103	50	3	existing	exist	VERB
hjic-103	50	4	criteria	criterion	NOUN
hjic-103	50	5	,	,	PUNCT
hjic-103	50	6	the	the	DET
hjic-103	50	7	d	d	ADJ
hjic-103	50	8	-	-	ADJ
hjic-103	50	9	optimal	optimal	ADJ
hjic-103	50	10	criterion	criterion	NOUN
hjic-103	50	11	and	and	CCONJ
hjic-103	50	12	the	the	DET
hjic-103	50	13	modified	modify	VERB
hjic-103	50	14	eoptimal	eoptimal	ADJ
hjic-103	50	15	criterion	criterion	NOUN
hjic-103	50	16	suggested	suggest	VERB
hjic-103	50	17	by	by	ADP
hjic-103	50	18	bernaerts	bernaert	NOUN
hjic-103	50	19	et	et	PROPN
hjic-103	50	20	al	al	PROPN
hjic-103	50	21	.	.	PUNCT
hjic-103	51	1	[	[	X
hjic-103	51	2	1	1	NUM
hjic-103	51	3	]	]	PUNCT
hjic-103	51	4	are	be	AUX
hjic-103	51	5	studied	study	VERB
hjic-103	51	6	in	in	ADP
hjic-103	51	7	this	this	DET
hjic-103	51	8	work	work	NOUN
hjic-103	51	9	.	.	PUNCT
hjic-103	52	1	i.	i.	PROPN
hjic-103	52	2	the	the	DET
hjic-103	52	3	d	d	ADJ
hjic-103	52	4	-	-	ADJ
hjic-103	52	5	optimal	optimal	ADJ
hjic-103	52	6	criterion	criterion	NOUN
hjic-103	52	7	minimizes	minimize	VERB
hjic-103	52	8	the	the	DET
hjic-103	52	9	determinant	determinant	NOUN
hjic-103	52	10	of	of	ADP
hjic-103	52	11	the	the	DET
hjic-103	52	12	covariance	covariance	NOUN
hjic-103	52	13	matrix	matrix	NOUN
hjic-103	52	14	,	,	PUNCT
hjic-103	52	15	and	and	CCONJ
hjic-103	52	16	thus	thus	ADV
hjic-103	52	17	minimizes	minimize	VERB
hjic-103	52	18	the	the	DET
hjic-103	52	19	volume	volume	NOUN
hjic-103	52	20	of	of	ADP
hjic-103	52	21	the	the	DET
hjic-103	52	22	joint	joint	ADJ
hjic-103	52	23	confidence	confidence	NOUN
hjic-103	52	24	region	region	NOUN
hjic-103	52	25	:	:	PUNCT
hjic-103	52	26	(	(	PUNCT
hjic-103	52	27	)	)	PUNCT
hjic-103	52	28	(	(	PUNCT
hjic-103	52	29	)	)	PUNCT
hjic-103	52	30	(	(	PUNCT
hjic-103	52	31	)	)	PUNCT
hjic-103	52	32	f	f	PROPN
hjic-103	52	33	f	f	PROPN
hjic-103	52	34	det	det	PROPN
hjic-103	52	35	max	max	PROPN
hjic-103	52	36	=	=	PROPN
hjic-103	52	37	d	d	PROPN
hjic-103	52	38	dtu	dtu	PROPN
hjic-103	52	39	j	j	PROPN
hjic-103	52	40	j	j	PROPN
hjic-103	52	41	(	(	PUNCT
hjic-103	52	42	5	5	NUM
hjic-103	52	43	)	)	PUNCT
hjic-103	52	44	ii	ii	NOUN
hjic-103	52	45	.	.	PUNCT
hjic-103	53	1	e	e	X
hjic-103	53	2	-	-	ADJ
hjic-103	53	3	optimal	optimal	ADJ
hjic-103	53	4	criterion	criterion	NOUN
hjic-103	53	5	minimizes	minimize	VERB
hjic-103	53	6	the	the	DET
hjic-103	53	7	condition	condition	NOUN
hjic-103	53	8	number	number	NOUN
hjic-103	53	9	of	of	ADP
hjic-103	53	10	f	f	PROPN
hjic-103	53	11	,	,	PUNCT
hjic-103	53	12	i.e.	i.e.	X
hjic-103	53	13	the	the	DET
hjic-103	53	14	ratio	ratio	NOUN
hjic-103	53	15	of	of	ADP
hjic-103	53	16	the	the	DET
hjic-103	53	17	largest	large	ADJ
hjic-103	53	18	to	to	ADP
hjic-103	53	19	the	the	DET
hjic-103	53	20	smallest	small	ADJ
hjic-103	53	21	eigenvalue	eigenvalue	NOUN
hjic-103	53	22	of	of	ADP
hjic-103	53	23	f	f	NOUN
hjic-103	53	24	:	:	PUNCT
hjic-103	53	25	(	(	PUNCT
hjic-103	53	26	)	)	PUNCT
hjic-103	53	27	(	(	PUNCT
hjic-103	53	28	)	)	PUNCT
hjic-103	53	29	(	(	PUNCT
hjic-103	53	30	)	)	PUNCT
hjic-103	53	31	(	(	PUNCT
hjic-103	53	32	)	)	PUNCT
hjic-103	53	33	(	(	PUNCT
hjic-103	53	34	)	)	PUNCT
hjic-103	53	35	f	f	X
hjic-103	53	36	ff	ff	NOUN
hjic-103	53	37	f	f	PROPN
hjic-103	53	38	min	min	PROPN
hjic-103	53	39	max	max	PROPN
hjic-103	53	40	min	min	PROPN
hjic-103	53	41	λ	λ	PROPN
hjic-103	53	42	λ	λ	PROPN
hjic-103	54	1	=	=	PROPN
hjic-103	54	2	e	e	PROPN
hjic-103	54	3	etu	etu	PROPN
hjic-103	54	4	j	j	PROPN
hjic-103	54	5	j	j	PROPN
hjic-103	54	6	(	(	PUNCT
hjic-103	54	7	6	6	NUM
hjic-103	54	8	)	)	PUNCT
hjic-103	54	9	these	these	DET
hjic-103	54	10	values	value	NOUN
hjic-103	54	11	correspond	correspond	VERB
hjic-103	54	12	to	to	ADP
hjic-103	54	13	the	the	DET
hjic-103	54	14	uncertainty	uncertainty	NOUN
hjic-103	54	15	of	of	ADP
hjic-103	54	16	the	the	DET
hjic-103	54	17	parameter	parameter	NOUN
hjic-103	54	18	estimation	estimation	NOUN
hjic-103	54	19	problem	problem	NOUN
hjic-103	54	20	.	.	PUNCT
hjic-103	55	1	fig	fig	NOUN
hjic-103	55	2	.	.	PUNCT
hjic-103	56	1	1	1	NUM
hjic-103	56	2	and	and	CCONJ
hjic-103	56	3	2	2	NUM
hjic-103	56	4	illustrate	illustrate	VERB
hjic-103	56	5	the	the	DET
hjic-103	56	6	effect	effect	NOUN
hjic-103	56	7	of	of	ADP
hjic-103	56	8	the	the	DET
hjic-103	56	9	input	input	NOUN
hjic-103	56	10	profile	profile	NOUN
hjic-103	56	11	on	on	ADP
hjic-103	56	12	the	the	DET
hjic-103	56	13	model	model	NOUN
hjic-103	56	14	output	output	NOUN
hjic-103	56	15	in	in	ADP
hjic-103	56	16	case	case	NOUN
hjic-103	56	17	of	of	ADP
hjic-103	56	18	the	the	DET
hjic-103	56	19	case	case	NOUN
hjic-103	56	20	study	study	NOUN
hjic-103	56	21	that	that	PRON
hjic-103	56	22	will	will	AUX
hjic-103	56	23	be	be	AUX
hjic-103	56	24	presented	present	VERB
hjic-103	56	25	later	later	ADV
hjic-103	56	26	in	in	ADP
hjic-103	56	27	the	the	DET
hjic-103	56	28	application	application	NOUN
hjic-103	56	29	example	example	NOUN
hjic-103	56	30	session	session	NOUN
hjic-103	56	31	.	.	PUNCT
hjic-103	57	1	the	the	DET
hjic-103	57	2	contour	contour	NOUN
hjic-103	57	3	plots	plot	NOUN
hjic-103	57	4	show	show	VERB
hjic-103	57	5	the	the	DET
hjic-103	57	6	square	square	ADJ
hjic-103	57	7	error	error	NOUN
hjic-103	57	8	of	of	ADP
hjic-103	57	9	model	model	NOUN
hjic-103	57	10	output	output	NOUN
hjic-103	57	11	with	with	ADP
hjic-103	57	12	respect	respect	NOUN
hjic-103	57	13	to	to	ADP
hjic-103	57	14	its	its	PRON
hjic-103	57	15	parameters	parameter	NOUN
hjic-103	57	16	around	around	ADP
hjic-103	57	17	the	the	DET
hjic-103	57	18	p0	p0	NOUN
hjic-103	57	19	nominal	nominal	ADJ
hjic-103	57	20	parameters	parameter	NOUN
hjic-103	57	21	:	:	PUNCT
hjic-103	57	22	(	(	PUNCT
hjic-103	57	23	)	)	PUNCT
hjic-103	57	24	(	(	PUNCT
hjic-103	57	25	)	)	PUNCT
hjic-103	57	26	(	(	PUNCT
hjic-103	57	27	)	)	PUNCT
hjic-103	57	28	(	(	PUNCT
hjic-103	57	29	∫	∫	PROPN
hjic-103	57	30	=	=	PROPN
hjic-103	57	31	−=	−=	NOUN
hjic-103	57	32	ft	ft	PROPN
hjic-103	57	33	f	f	X
hjic-103	58	1	i	i	PRON
hjic-103	58	2	tty	tty	VERB
hjic-103	58	3	t	t	PROPN
hjic-103	58	4	j	j	PROPN
hjic-103	58	5	0	0	NUM
hjic-103	58	6	t	t	PROPN
hjic-103	58	7	20	20	NUM
hjic-103	58	8	d	d	PROPN
hjic-103	58	9	,	,	PUNCT
hjic-103	58	10	t	t	PROPN
hjic-103	58	11	,	,	PUNCT
hjic-103	58	12	y1	y1	NOUN
hjic-103	58	13	ppp	ppp	NOUN
hjic-103	58	14	)	)	PUNCT
hjic-103	58	15	(	(	PUNCT
hjic-103	58	16	7	7	X
hjic-103	58	17	)	)	PUNCT
hjic-103	58	18	one	one	NOUN
hjic-103	58	19	can	can	AUX
hjic-103	58	20	see	see	VERB
hjic-103	58	21	that	that	SCONJ
hjic-103	58	22	when	when	SCONJ
hjic-103	58	23	an	an	DET
hjic-103	58	24	e	e	NOUN
hjic-103	58	25	-	-	ADJ
hjic-103	58	26	optimal	optimal	ADJ
hjic-103	58	27	input	input	NOUN
hjic-103	58	28	profile	profile	NOUN
hjic-103	58	29	is	be	AUX
hjic-103	58	30	used	use	VERB
hjic-103	58	31	,	,	PUNCT
hjic-103	58	32	then	then	ADV
hjic-103	58	33	the	the	DET
hjic-103	58	34	parameter	parameter	NOUN
hjic-103	58	35	uncertainty	uncertainty	NOUN
hjic-103	58	36	region	region	NOUN
hjic-103	58	37	is	be	AUX
hjic-103	58	38	smaller	small	ADJ
hjic-103	58	39	.	.	PUNCT
hjic-103	59	1	this	this	PRON
hjic-103	59	2	means	mean	VERB
hjic-103	59	3	that	that	SCONJ
hjic-103	59	4	,	,	PUNCT
hjic-103	59	5	if	if	SCONJ
hjic-103	59	6	the	the	DET
hjic-103	59	7	p0	p0	NOUN
hjic-103	59	8	nominal	nominal	ADJ
hjic-103	59	9	parameters	parameter	NOUN
hjic-103	59	10	are	be	AUX
hjic-103	59	11	close	close	ADJ
hjic-103	59	12	to	to	ADP
hjic-103	59	13	the	the	DET
hjic-103	59	14	optimal	optimal	ADJ
hjic-103	59	15	p	p	PROPN
hjic-103	59	16	*	*	ADJ
hjic-103	59	17	parameters	parameter	NOUN
hjic-103	59	18	,	,	PUNCT
hjic-103	59	19	the	the	DET
hjic-103	59	20	parameter	parameter	NOUN
hjic-103	59	21	estimation	estimation	NOUN
hjic-103	59	22	based	base	VERB
hjic-103	59	23	on	on	ADP
hjic-103	59	24	this	this	DET
hjic-103	59	25	profile	profile	NOUN
hjic-103	59	26	(	(	PUNCT
hjic-103	59	27	fig	fig	NOUN
hjic-103	59	28	.	.	NOUN
hjic-103	60	1	1	1	NUM
hjic-103	60	2	)	)	PUNCT
hjic-103	60	3	most	most	ADV
hjic-103	60	4	likely	likely	ADJ
hjic-103	60	5	results	result	NOUN
hjic-103	60	6	in	in	ADP
hjic-103	60	7	accurate	accurate	ADJ
hjic-103	60	8	parameters	parameter	NOUN
hjic-103	60	9	than	than	ADP
hjic-103	60	10	the	the	DET
hjic-103	60	11	estimation	estimation	NOUN
hjic-103	60	12	based	base	VERB
hjic-103	60	13	on	on	ADP
hjic-103	60	14	a	a	DET
hjic-103	60	15	manually	manually	ADV
hjic-103	60	16	selected	select	VERB
hjic-103	60	17	profile	profile	NOUN
hjic-103	60	18	(	(	PUNCT
hjic-103	60	19	fig	fig	NOUN
hjic-103	60	20	.	.	PUNCT
hjic-103	61	1	2	2	NUM
hjic-103	61	2	)	)	PUNCT
hjic-103	61	3	.	.	PUNCT
hjic-103	62	1	these	these	DET
hjic-103	62	2	figures	figure	NOUN
hjic-103	62	3	suggest	suggest	VERB
hjic-103	62	4	that	that	SCONJ
hjic-103	62	5	when	when	SCONJ
hjic-103	62	6	one	one	PRON
hjic-103	62	7	has	have	VERB
hjic-103	62	8	only	only	ADV
hjic-103	62	9	a	a	DET
hjic-103	62	10	draft	draft	NOUN
hjic-103	62	11	estimate	estimate	NOUN
hjic-103	62	12	on	on	ADP
hjic-103	62	13	the	the	DET
hjic-103	62	14	parameters	parameter	NOUN
hjic-103	62	15	of	of	ADP
hjic-103	62	16	a	a	DET
hjic-103	62	17	complex	complex	ADJ
hjic-103	62	18	dynamical	dynamical	ADJ
hjic-103	62	19	model	model	NOUN
hjic-103	62	20	,	,	PUNCT
hjic-103	62	21	he	he	PRON
hjic-103	62	22	/	/	PUNCT
hjic-103	62	23	she	she	PRON
hjic-103	62	24	should	should	AUX
hjic-103	62	25	use	use	VERB
hjic-103	62	26	it	it	PRON
hjic-103	62	27	to	to	PART
hjic-103	62	28	design	design	VERB
hjic-103	62	29	an	an	DET
hjic-103	62	30	u(t	u(t	NOUN
hjic-103	62	31	)	)	PUNCT
hjic-103	62	32	input	input	NOUN
hjic-103	62	33	profile	profile	NOUN
hjic-103	62	34	for	for	ADP
hjic-103	62	35	a	a	DET
hjic-103	62	36	parameter	parameter	NOUN
hjic-103	62	37	estimation	estimation	NOUN
hjic-103	62	38	procedure	procedure	NOUN
hjic-103	62	39	(	(	PUNCT
hjic-103	62	40	eq	eq	NOUN
hjic-103	62	41	.	.	NOUN
hjic-103	62	42	2	2	NUM
hjic-103	62	43	-	-	SYM
hjic-103	62	44	3	3	NUM
hjic-103	62	45	)	)	PUNCT
hjic-103	62	46	rather	rather	ADV
hjic-103	62	47	than	than	SCONJ
hjic-103	62	48	to	to	PART
hjic-103	62	49	use	use	VERB
hjic-103	62	50	data	datum	NOUN
hjic-103	62	51	taken	take	VERB
hjic-103	62	52	from	from	ADP
hjic-103	62	53	a	a	DET
hjic-103	62	54	non	non	ADJ
hjic-103	62	55	-	-	ADJ
hjic-103	62	56	optimized	optimize	VERB
hjic-103	62	57	input	input	NOUN
hjic-103	62	58	profile	profile	NOUN
hjic-103	62	59	for	for	ADP
hjic-103	62	60	the	the	DET
hjic-103	62	61	identification	identification	NOUN
hjic-103	62	62	.	.	PUNCT
hjic-103	63	1	in	in	ADP
hjic-103	63	2	the	the	DET
hjic-103	63	3	following	follow	VERB
hjic-103	63	4	session	session	NOUN
hjic-103	63	5	a	a	DET
hjic-103	63	6	new	new	ADJ
hjic-103	63	7	optimization	optimization	NOUN
hjic-103	63	8	algorithm	algorithm	NOUN
hjic-103	63	9	will	will	AUX
hjic-103	63	10	be	be	AUX
hjic-103	63	11	presented	present	VERB
hjic-103	63	12	for	for	ADP
hjic-103	63	13	the	the	DET
hjic-103	63	14	effective	effective	ADJ
hjic-103	63	15	design	design	NOUN
hjic-103	63	16	of	of	ADP
hjic-103	63	17	the	the	DET
hjic-103	63	18	experiments	experiment	NOUN
hjic-103	63	19	.	.	PUNCT
hjic-103	64	1	for	for	ADP
hjic-103	64	2	nonlinear	nonlinear	ADJ
hjic-103	64	3	models	model	NOUN
hjic-103	64	4	,	,	PUNCT
hjic-103	64	5	oed	oed	PROPN
hjic-103	64	6	results	result	NOUN
hjic-103	64	7	in	in	ADP
hjic-103	64	8	an	an	DET
hjic-103	64	9	iterative	iterative	NOUN
hjic-103	64	10	procedure	procedure	NOUN
hjic-103	64	11	due	due	ADP
hjic-103	64	12	to	to	ADP
hjic-103	64	13	the	the	DET
hjic-103	64	14	fact	fact	NOUN
hjic-103	64	15	that	that	SCONJ
hjic-103	64	16	the	the	DET
hjic-103	64	17	parameters	parameter	NOUN
hjic-103	64	18	of	of	ADP
hjic-103	64	19	the	the	DET
hjic-103	64	20	designed	design	VERB
hjic-103	64	21	experiment	experiment	NOUN
hjic-103	64	22	depends	depend	VERB
hjic-103	64	23	on	on	ADP
hjic-103	64	24	the	the	DET
hjic-103	64	25	model	model	NOUN
hjic-103	64	26	parameters	parameter	NOUN
hjic-103	64	27	itself	itself	PRON
hjic-103	64	28	,	,	PUNCT
hjic-103	64	29	see	see	VERB
hjic-103	64	30	fig	fig	NOUN
hjic-103	64	31	.	.	PUNCT
hjic-103	65	1	3	3	NUM
hjic-103	65	2	.	.	NOUN
hjic-103	65	3	0	0	NUM
hjic-103	65	4	5	5	NUM
hjic-103	65	5	10	10	NUM
hjic-103	65	6	15	15	NUM
hjic-103	65	7	20	20	NUM
hjic-103	65	8	25	25	NUM
hjic-103	65	9	30	30	NUM
hjic-103	65	10	35	35	NUM
hjic-103	65	11	40	40	NUM
hjic-103	65	12	0	0	NUM
hjic-103	65	13	0.1	0.1	NUM
hjic-103	65	14	0.2	0.2	NUM
hjic-103	65	15	t	t	NOUN
hjic-103	65	16	u	u	NOUN
hjic-103	65	17	µm/µm	µm/µm	NUM
hjic-103	65	18	0	0	NUM
hjic-103	65	19	k	k	PROPN
hjic-103	65	20	s	s	PROPN
hjic-103	65	21	/	/	SYM
hjic-103	65	22	k	k	PROPN
hjic-103	65	23	s0	s0	PROPN
hjic-103	65	24	0.99	0.99	NUM
hjic-103	65	25	0.995	0.995	NUM
hjic-103	65	26	1	1	NUM
hjic-103	65	27	1.005	1.005	NUM
hjic-103	65	28	1.01	1.01	NUM
hjic-103	65	29	0.9	0.9	NUM
hjic-103	65	30	1	1	NUM
hjic-103	65	31	1.1	1.1	NUM
hjic-103	65	32	10	10	NUM
hjic-103	65	33	20	20	NUM
hjic-103	65	34	30	30	NUM
hjic-103	65	35	fig	fig	NOUN
hjic-103	65	36	.	.	PUNCT
hjic-103	65	37	1	1	NUM
hjic-103	65	38	contour	contour	NOUN
hjic-103	65	39	plots	plot	NOUN
hjic-103	65	40	of	of	ADP
hjic-103	65	41	the	the	DET
hjic-103	65	42	identification	identification	NOUN
hjic-103	65	43	cost	cost	NOUN
hjic-103	65	44	(	(	PUNCT
hjic-103	65	45	ji	ji	NOUN
hjic-103	65	46	)	)	PUNCT
hjic-103	65	47	with	with	ADP
hjic-103	65	48	respect	respect	NOUN
hjic-103	65	49	to	to	ADP
hjic-103	65	50	parameters	parameter	NOUN
hjic-103	65	51	for	for	ADP
hjic-103	65	52	an	an	DET
hjic-103	65	53	e	e	NOUN
hjic-103	65	54	-	-	ADJ
hjic-103	65	55	optimized	optimize	VERB
hjic-103	65	56	feeding	feeding	NOUN
hjic-103	65	57	profile	profile	NOUN
hjic-103	65	58	115	115	NUM
hjic-103	65	59	0	0	NUM
hjic-103	65	60	5	5	NUM
hjic-103	65	61	10	10	NUM
hjic-103	65	62	15	15	NUM
hjic-103	65	63	20	20	NUM
hjic-103	65	64	25	25	NUM
hjic-103	65	65	30	30	NUM
hjic-103	65	66	35	35	NUM
hjic-103	65	67	40	40	NUM
hjic-103	65	68	0	0	NUM
hjic-103	65	69	0.1	0.1	NUM
hjic-103	65	70	0.2	0.2	NUM
hjic-103	65	71	t	t	NOUN
hjic-103	65	72	u	u	NOUN
hjic-103	65	73	µm/µm	µm/µm	NUM
hjic-103	65	74	0	0	NUM
hjic-103	66	1	k	k	PROPN
hjic-103	66	2	s	s	PROPN
hjic-103	66	3	/	/	SYM
hjic-103	66	4	k	k	PROPN
hjic-103	66	5	s0	s0	PROPN
hjic-103	66	6	0.99	0.99	NUM
hjic-103	66	7	0.995	0.995	NUM
hjic-103	66	8	1	1	NUM
hjic-103	66	9	1.005	1.005	NUM
hjic-103	66	10	1.01	1.01	NUM
hjic-103	66	11	0.9	0.9	NUM
hjic-103	66	12	1	1	NUM
hjic-103	66	13	1.1	1.1	NUM
hjic-103	66	14	100	100	NUM
hjic-103	66	15	200	200	NUM
hjic-103	66	16	300	300	NUM
hjic-103	66	17	400	400	NUM
hjic-103	66	18	500	500	NUM
hjic-103	66	19	fig	fig	NOUN
hjic-103	66	20	.	.	PUNCT
hjic-103	67	1	2	2	NUM
hjic-103	67	2	contour	contour	NOUN
hjic-103	67	3	plots	plot	NOUN
hjic-103	67	4	of	of	ADP
hjic-103	67	5	the	the	DET
hjic-103	67	6	identification	identification	NOUN
hjic-103	67	7	cost	cost	NOUN
hjic-103	67	8	(	(	PUNCT
hjic-103	67	9	ji	ji	NOUN
hjic-103	67	10	)	)	PUNCT
hjic-103	67	11	with	with	ADP
hjic-103	67	12	respect	respect	NOUN
hjic-103	67	13	to	to	ADP
hjic-103	67	14	parameters	parameter	NOUN
hjic-103	67	15	for	for	ADP
hjic-103	67	16	a	a	DET
hjic-103	67	17	manually	manually	ADV
hjic-103	67	18	selected	select	VERB
hjic-103	67	19	feeding	feeding	NOUN
hjic-103	67	20	profile	profile	NOUN
hjic-103	67	21	initial	initial	ADJ
hjic-103	67	22	parameters	parameter	NOUN
hjic-103	67	23	experiment	experiment	NOUN
hjic-103	67	24	design	design	NOUN
hjic-103	67	25	experiment	experiment	NOUN
hjic-103	67	26	parameter	parameter	NOUN
hjic-103	67	27	estimation	estimation	NOUN
hjic-103	67	28	more	more	ADJ
hjic-103	67	29	experiment	experiment	NOUN
hjic-103	67	30	?	?	PUNCT
hjic-103	68	1	end	end	NOUN
hjic-103	68	2	p0	p0	NOUN
hjic-103	68	3	u(t	u(t	NOUN
hjic-103	68	4	)	)	PUNCT
hjic-103	68	5	)	)	PUNCT
hjic-103	69	1	(	(	PUNCT
hjic-103	69	2	~	~	PUNCT
hjic-103	69	3	ty	ty	PRON
hjic-103	69	4	p0	p0	NOUN
hjic-103	69	5	yes	yes	INTJ
hjic-103	69	6	no	no	DET
hjic-103	69	7	fig	fig	NOUN
hjic-103	69	8	.	.	PUNCT
hjic-103	70	1	3	3	NUM
hjic-103	70	2	scheme	scheme	NOUN
hjic-103	70	3	of	of	ADP
hjic-103	70	4	parameter	parameter	NOUN
hjic-103	70	5	estimation	estimation	NOUN
hjic-103	70	6	with	with	ADP
hjic-103	70	7	oed	oed	PROPN
hjic-103	70	8	both	both	CCONJ
hjic-103	70	9	the	the	DET
hjic-103	70	10	parameter	parameter	NOUN
hjic-103	70	11	estimation	estimation	NOUN
hjic-103	70	12	and	and	CCONJ
hjic-103	70	13	the	the	DET
hjic-103	70	14	experiment	experiment	NOUN
hjic-103	70	15	design	design	NOUN
hjic-103	70	16	steps	step	NOUN
hjic-103	70	17	of	of	ADP
hjic-103	70	18	this	this	DET
hjic-103	70	19	iterative	iterative	NOUN
hjic-103	70	20	scheme	scheme	NOUN
hjic-103	70	21	represent	represent	VERB
hjic-103	70	22	a	a	DET
hjic-103	70	23	complex	complex	ADJ
hjic-103	70	24	nonlinear	nonlinear	ADJ
hjic-103	70	25	optimization	optimization	NOUN
hjic-103	70	26	problem	problem	NOUN
hjic-103	70	27	,	,	PUNCT
hjic-103	70	28	hence	hence	ADV
hjic-103	70	29	the	the	DET
hjic-103	70	30	effectiveness	effectiveness	NOUN
hjic-103	70	31	of	of	ADP
hjic-103	70	32	the	the	DET
hjic-103	70	33	applied	apply	VERB
hjic-103	70	34	optimization	optimization	NOUN
hjic-103	70	35	algorithms	algorithm	NOUN
hjic-103	70	36	have	have	VERB
hjic-103	70	37	great	great	ADJ
hjic-103	70	38	influence	influence	NOUN
hjic-103	70	39	on	on	ADP
hjic-103	70	40	the	the	DET
hjic-103	70	41	performance	performance	NOUN
hjic-103	70	42	of	of	ADP
hjic-103	70	43	the	the	DET
hjic-103	70	44	whole	whole	ADJ
hjic-103	70	45	procedure	procedure	NOUN
hjic-103	70	46	.	.	PUNCT
hjic-103	71	1	the	the	DET
hjic-103	71	2	classical	classical	ADJ
hjic-103	71	3	solution	solution	NOUN
hjic-103	71	4	is	be	AUX
hjic-103	71	5	to	to	PART
hjic-103	71	6	use	use	VERB
hjic-103	71	7	nonlinear	nonlinear	ADJ
hjic-103	71	8	least	least	ADJ
hjic-103	71	9	squares	square	NOUN
hjic-103	71	10	(	(	PUNCT
hjic-103	71	11	nls	nls	NOUN
hjic-103	71	12	)	)	PUNCT
hjic-103	71	13	algorithm	algorithm	NOUN
hjic-103	71	14	for	for	ADP
hjic-103	71	15	parameter	parameter	NOUN
hjic-103	71	16	estimation	estimation	NOUN
hjic-103	71	17	eq	eq	ADP
hjic-103	71	18	.	.	PROPN
hjic-103	71	19	2	2	NUM
hjic-103	71	20	-	-	SYM
hjic-103	71	21	3	3	NUM
hjic-103	71	22	,	,	PUNCT
hjic-103	71	23	and	and	CCONJ
hjic-103	71	24	sequential	sequential	ADJ
hjic-103	71	25	quadratic	quadratic	ADJ
hjic-103	71	26	programming	programming	NOUN
hjic-103	71	27	(	(	PUNCT
hjic-103	71	28	sqp	sqp	PROPN
hjic-103	71	29	)	)	PUNCT
hjic-103	71	30	for	for	ADP
hjic-103	71	31	the	the	DET
hjic-103	71	32	experiment	experiment	NOUN
hjic-103	71	33	design	design	NOUN
hjic-103	71	34	eq	eq	ADP
hjic-103	71	35	.	.	PROPN
hjic-103	71	36	5	5	NUM
hjic-103	71	37	or	or	CCONJ
hjic-103	71	38	eq	eq	NOUN
hjic-103	71	39	.	.	PROPN
hjic-103	71	40	6	6	NUM
hjic-103	71	41	.	.	PUNCT
hjic-103	71	42	evolutionary	evolutionary	ADJ
hjic-103	71	43	strategy	strategy	NOUN
hjic-103	71	44	this	this	DET
hjic-103	71	45	paper	paper	NOUN
hjic-103	71	46	proposes	propose	VERB
hjic-103	71	47	the	the	DET
hjic-103	71	48	application	application	NOUN
hjic-103	71	49	of	of	ADP
hjic-103	71	50	evolutionary	evolutionary	ADJ
hjic-103	71	51	strategy	strategy	NOUN
hjic-103	71	52	(	(	PUNCT
hjic-103	71	53	es	es	NOUN
hjic-103	71	54	)	)	PUNCT
hjic-103	71	55	instead	instead	ADV
hjic-103	71	56	of	of	ADP
hjic-103	71	57	the	the	DET
hjic-103	71	58	utilization	utilization	NOUN
hjic-103	71	59	of	of	ADP
hjic-103	71	60	nls	nls	NOUN
hjic-103	71	61	and	and	CCONJ
hjic-103	71	62	sqp	sqp	PROPN
hjic-103	71	63	.	.	PROPN
hjic-103	71	64	es	es	PROPN
hjic-103	71	65	is	be	AUX
hjic-103	71	66	a	a	DET
hjic-103	71	67	stochastic	stochastic	ADJ
hjic-103	71	68	optimization	optimization	NOUN
hjic-103	71	69	algorithm	algorithm	NOUN
hjic-103	71	70	that	that	PRON
hjic-103	71	71	uses	use	VERB
hjic-103	71	72	the	the	DET
hjic-103	71	73	model	model	NOUN
hjic-103	71	74	of	of	ADP
hjic-103	71	75	natural	natural	ADJ
hjic-103	71	76	selection	selection	NOUN
hjic-103	71	77	.	.	PUNCT
hjic-103	72	1	the	the	DET
hjic-103	72	2	advantage	advantage	NOUN
hjic-103	72	3	of	of	ADP
hjic-103	72	4	es	es	NOUN
hjic-103	72	5	is	be	AUX
hjic-103	72	6	that	that	SCONJ
hjic-103	72	7	it	it	PRON
hjic-103	72	8	has	have	AUX
hjic-103	72	9	proved	prove	VERB
hjic-103	72	10	particularly	particularly	ADV
hjic-103	72	11	successful	successful	ADJ
hjic-103	72	12	in	in	ADP
hjic-103	72	13	problems	problem	NOUN
hjic-103	72	14	that	that	PRON
hjic-103	72	15	are	be	AUX
hjic-103	72	16	highly	highly	ADV
hjic-103	72	17	nonlinear	nonlinear	ADJ
hjic-103	72	18	,	,	PUNCT
hjic-103	72	19	that	that	PRON
hjic-103	72	20	are	be	AUX
hjic-103	72	21	stochastic	stochastic	ADJ
hjic-103	72	22	,	,	PUNCT
hjic-103	72	23	and	and	CCONJ
hjic-103	72	24	that	that	PRON
hjic-103	72	25	are	be	AUX
hjic-103	72	26	poorly	poorly	ADV
hjic-103	72	27	understood	understand	VERB
hjic-103	72	28	[	[	X
hjic-103	72	29	7	7	NUM
hjic-103	72	30	]	]	PUNCT
hjic-103	72	31	.	.	PUNCT
hjic-103	73	1	evolution	evolution	NOUN
hjic-103	73	2	strategy	strategy	NOUN
hjic-103	73	3	is	be	AUX
hjic-103	73	4	the	the	DET
hjic-103	73	5	member	member	NOUN
hjic-103	73	6	of	of	ADP
hjic-103	73	7	evolutionary	evolutionary	ADJ
hjic-103	73	8	algorithms	algorithm	NOUN
hjic-103	73	9	.	.	PUNCT
hjic-103	74	1	the	the	DET
hjic-103	74	2	design	design	NOUN
hjic-103	74	3	variables	variable	NOUN
hjic-103	74	4	in	in	ADP
hjic-103	74	5	es	es	NOUN
hjic-103	74	6	are	be	AUX
hjic-103	74	7	represented	represent	VERB
hjic-103	74	8	by	by	ADP
hjic-103	74	9	n	n	CCONJ
hjic-103	74	10	-	-	PUNCT
hjic-103	74	11	dimensional	dimensional	ADJ
hjic-103	74	12	vector	vector	NOUN
hjic-103	74	13	,	,	PUNCT
hjic-103	74	14	where	where	SCONJ
hjic-103	74	15	x	x	X
hjic-103	74	16	[	[	PUNCT
hjic-103	74	17	]	]	X
hjic-103	74	18	t,2,1	t,2,1	NOUN
hjic-103	74	19	,	,	PUNCT
hjic-103	74	20	,	,	PUNCT
hjic-103	74	21	,	,	PUNCT
hjic-103	74	22	,	,	PUNCT
hjic-103	74	23	njjjj	njjjj	NOUN
hjic-103	74	24	xxx	xxx	X
hjic-103	75	1	k	k	X
hjic-103	75	2	=	=	NOUN
hjic-103	75	3	x	x	SYM
hjic-103	75	4	j	j	PROPN
hjic-103	75	5	represents	represent	VERB
hjic-103	75	6	the	the	DET
hjic-103	75	7	jth	jth	PROPN
hjic-103	75	8	potential	potential	ADJ
hjic-103	75	9	solution	solution	NOUN
hjic-103	75	10	,	,	PUNCT
hjic-103	75	11	i.e.	i.e.	X
hjic-103	75	12	the	the	DET
hjic-103	75	13	jth	jth	PROPN
hjic-103	75	14	the	the	DET
hjic-103	75	15	member	member	NOUN
hjic-103	75	16	of	of	ADP
hjic-103	75	17	the	the	DET
hjic-103	75	18	population	population	NOUN
hjic-103	75	19	.	.	PUNCT
hjic-103	76	1	the	the	DET
hjic-103	76	2	mutation	mutation	NOUN
hjic-103	76	3	operator	operator	NOUN
hjic-103	76	4	adds	add	VERB
hjic-103	76	5	zj	zj	PROPN
hjic-103	76	6	,	,	PUNCT
hjic-103	76	7	i	i	PRON
hjic-103	76	8	normal	normal	ADJ
hjic-103	76	9	distributed	distribute	VERB
hjic-103	76	10	random	random	ADJ
hjic-103	76	11	numbers	number	NOUN
hjic-103	76	12	to	to	ADP
hjic-103	76	13	the	the	DET
hjic-103	76	14	design	design	NOUN
hjic-103	76	15	variables	variable	NOUN
hjic-103	76	16	:	:	PUNCT
hjic-103	76	17	xj	xj	PROPN
hjic-103	76	18	,	,	PUNCT
hjic-103	76	19	i	i	PROPN
hjic-103	76	20	=	=	SYM
hjic-103	76	21	xj	xj	PROPN
hjic-103	76	22	,	,	PUNCT
hjic-103	76	23	i	i	PROPN
hjic-103	76	24	+	+	PROPN
hjic-103	76	25	zj	zj	PROPN
hjic-103	76	26	,	,	PUNCT
hjic-103	76	27	i	i	PRON
hjic-103	76	28	,	,	PUNCT
hjic-103	76	29	where	where	SCONJ
hjic-103	76	30	zj	zj	PROPN
hjic-103	76	31	,	,	PUNCT
hjic-103	76	32	i	i	PRON
hjic-103	76	33	=	=	PUNCT
hjic-103	76	34	n(0,σj	n(0,σj	PROPN
hjic-103	76	35	,	,	PUNCT
hjic-103	76	36	i	i	PRON
hjic-103	76	37	)	)	PUNCT
hjic-103	76	38	is	be	AUX
hjic-103	76	39	a	a	DET
hjic-103	76	40	random	random	ADJ
hjic-103	76	41	number	number	NOUN
hjic-103	76	42	with	with	ADP
hjic-103	76	43	σj	σj	NOUN
hjic-103	76	44	,	,	PUNCT
hjic-103	76	45	i	i	PRON
hjic-103	76	46	standard	standard	ADJ
hjic-103	76	47	deviation	deviation	NOUN
hjic-103	76	48	.	.	PUNCT
hjic-103	77	1	to	to	PART
hjic-103	77	2	allow	allow	VERB
hjic-103	77	3	better	well	ADJ
hjic-103	77	4	adaptation	adaptation	NOUN
hjic-103	77	5	to	to	ADP
hjic-103	77	6	the	the	DET
hjic-103	77	7	objective	objective	ADJ
hjic-103	77	8	function	function	NOUN
hjic-103	77	9	’s	’s	PART
hjic-103	77	10	topology	topology	NOUN
hjic-103	77	11	,	,	PUNCT
hjic-103	77	12	the	the	DET
hjic-103	77	13	design	design	NOUN
hjic-103	77	14	variables	variable	NOUN
hjic-103	77	15	are	be	AUX
hjic-103	77	16	accompanied	accompany	VERB
hjic-103	77	17	by	by	ADP
hjic-103	77	18	these	these	DET
hjic-103	77	19	standard	standard	ADJ
hjic-103	77	20	deviation	deviation	NOUN
hjic-103	77	21	variables	variable	NOUN
hjic-103	77	22	which	which	PRON
hjic-103	77	23	are	be	AUX
hjic-103	77	24	so	so	ADV
hjic-103	77	25	-	-	PUNCT
hjic-103	77	26	called	call	VERB
hjic-103	77	27	strategy	strategy	NOUN
hjic-103	77	28	parameters	parameter	NOUN
hjic-103	77	29	.	.	PUNCT
hjic-103	78	1	hence	hence	ADV
hjic-103	78	2	the	the	DET
hjic-103	78	3	σj	σj	ADJ
hjic-103	78	4	strategy	strategy	NOUN
hjic-103	78	5	variables	variable	NOUN
hjic-103	78	6	control	control	VERB
hjic-103	78	7	the	the	DET
hjic-103	78	8	step	step	NOUN
hjic-103	78	9	size	size	NOUN
hjic-103	78	10	of	of	ADP
hjic-103	78	11	standard	standard	ADJ
hjic-103	78	12	deviations	deviation	NOUN
hjic-103	78	13	in	in	ADP
hjic-103	78	14	the	the	DET
hjic-103	78	15	mutation	mutation	NOUN
hjic-103	78	16	for	for	ADP
hjic-103	78	17	jth	jth	PROPN
hjic-103	78	18	individual	individual	PROPN
hjic-103	78	19	.	.	PUNCT
hjic-103	79	1	so	so	ADV
hjic-103	79	2	en	en	ADP
hjic-103	79	3	es	es	PROPN
hjic-103	79	4	-	-	PUNCT
hjic-103	79	5	individual	individual	ADJ
hjic-103	79	6	aj	aj	PROPN
hjic-103	79	7	=	=	SYM
hjic-103	79	8	(	(	PUNCT
hjic-103	79	9	xj	xj	PROPN
hjic-103	79	10	,	,	PUNCT
hjic-103	79	11	σj	σj	ADJ
hjic-103	79	12	)	)	PUNCT
hjic-103	79	13	consist	consist	NOUN
hjic-103	79	14	of	of	ADP
hjic-103	79	15	two	two	NUM
hjic-103	79	16	components	component	NOUN
hjic-103	79	17	:	:	PUNCT
hjic-103	79	18	the	the	DET
hjic-103	79	19	design	design	NOUN
hjic-103	79	20	variables	variable	NOUN
hjic-103	79	21	and	and	CCONJ
hjic-103	79	22	the	the	DET
hjic-103	79	23	strategy	strategy	NOUN
hjic-103	79	24	variables	variable	NOUN
hjic-103	79	25	.	.	PUNCT
hjic-103	80	1	before	before	SCONJ
hjic-103	80	2	the	the	DET
hjic-103	80	3	design	design	NOUN
hjic-103	80	4	variables	variable	NOUN
hjic-103	80	5	are	be	AUX
hjic-103	80	6	changed	change	VERB
hjic-103	80	7	by	by	ADP
hjic-103	80	8	mutation	mutation	NOUN
hjic-103	80	9	operator	operator	NOUN
hjic-103	80	10	,	,	PUNCT
hjic-103	80	11	the	the	DET
hjic-103	80	12	standard	standard	ADJ
hjic-103	80	13	deviations	deviation	NOUN
hjic-103	80	14	σj	σj	VERB
hjic-103	80	15	are	be	AUX
hjic-103	80	16	mutated	mutate	VERB
hjic-103	80	17	using	use	VERB
hjic-103	80	18	a	a	DET
hjic-103	80	19	multiplicative	multiplicative	ADJ
hjic-103	80	20	normally	normally	ADV
hjic-103	80	21	distributed	distribute	VERB
hjic-103	80	22	process	process	NOUN
hjic-103	80	23	:	:	PUNCT
hjic-103	80	24	(	(	PUNCT
hjic-103	80	25	)	)	PUNCT
hjic-103	80	26	(	(	PUNCT
hjic-103	80	27	)	)	PUNCT
hjic-103	80	28	(	(	PUNCT
hjic-103	80	29	)	)	PUNCT
hjic-103	80	30	1,0n1,0nexp	1,0n1,0nexp	NOUN
hjic-103	80	31	i	i	NOUN
hjic-103	80	32	)	)	PUNCT
hjic-103	80	33	1	1	NUM
hjic-103	80	34	(	(	PUNCT
hjic-103	80	35	,	,	PUNCT
hjic-103	80	36	)	)	PUNCT
hjic-103	80	37	(	(	PUNCT
hjic-103	80	38	,	,	PUNCT
hjic-103	80	39	⋅+⋅′=	⋅+⋅′=	PROPN
hjic-103	80	40	−	−	PROPN
hjic-103	80	41	ττσσ	ττσσ	NOUN
hjic-103	80	42	t	t	PROPN
hjic-103	81	1	ij	ij	INTJ
hjic-103	81	2	t	t	X
hjic-103	81	3	ij	ij	X
hjic-103	81	4	(	(	PUNCT
hjic-103	81	5	8)	8)	NUM
hjic-103	81	6	the	the	PRON
hjic-103	81	7	(	(	PUNCT
hjic-103	81	8	)	)	PUNCT
hjic-103	81	9	(	(	PUNCT
hjic-103	81	10	)	)	PUNCT
hjic-103	81	11	1,0nexp	1,0nexp	NUM
hjic-103	81	12	⋅′τ	⋅′τ	PROPN
hjic-103	81	13	is	be	AUX
hjic-103	81	14	a	a	DET
hjic-103	81	15	global	global	ADJ
hjic-103	81	16	factor	factor	NOUN
hjic-103	81	17	which	which	PRON
hjic-103	81	18	allows	allow	VERB
hjic-103	81	19	an	an	DET
hjic-103	81	20	overall	overall	ADJ
hjic-103	81	21	change	change	NOUN
hjic-103	81	22	of	of	ADP
hjic-103	81	23	the	the	DET
hjic-103	81	24	mutability	mutability	NOUN
hjic-103	81	25	,	,	PUNCT
hjic-103	81	26	and	and	CCONJ
hjic-103	81	27	the	the	DET
hjic-103	81	28	(	(	PUNCT
hjic-103	81	29	)	)	PUNCT
hjic-103	81	30	(	(	PUNCT
hjic-103	81	31	)	)	PUNCT
hjic-103	81	32	1,0nexp	1,0nexp	NUM
hjic-103	81	33	i⋅τ	i⋅τ	PROPN
hjic-103	81	34	allows	allow	VERB
hjic-103	81	35	individuals	individual	NOUN
hjic-103	81	36	to	to	PART
hjic-103	81	37	change	change	VERB
hjic-103	81	38	of	of	ADP
hjic-103	81	39	their	their	PRON
hjic-103	81	40	mean	mean	ADJ
hjic-103	81	41	step	step	NOUN
hjic-103	81	42	sizes	size	NOUN
hjic-103	81	43	σj	σj	VERB
hjic-103	81	44	,	,	PUNCT
hjic-103	81	45	i.	i.	PROPN
hjic-103	81	46	so	so	ADV
hjic-103	81	47	τ	τ	PROPN
hjic-103	81	48	’	'	PUNCT
hjic-103	81	49	and	and	CCONJ
hjic-103	81	50	τ	τ	PROPN
hjic-103	81	51	parameters	parameter	NOUN
hjic-103	81	52	can	can	AUX
hjic-103	81	53	be	be	AUX
hjic-103	81	54	interpreted	interpret	VERB
hjic-103	81	55	as	as	ADP
hjic-103	81	56	global	global	ADJ
hjic-103	81	57	learning	learning	NOUN
hjic-103	81	58	rates	rate	NOUN
hjic-103	81	59	.	.	PUNCT
hjic-103	82	1	schwefel	schwefel	NOUN
hjic-103	82	2	suggests	suggest	VERB
hjic-103	82	3	setting	set	VERB
hjic-103	82	4	them	they	PRON
hjic-103	82	5	as	as	ADP
hjic-103	82	6	[	[	X
hjic-103	82	7	8	8	NUM
hjic-103	82	8	]	]	SYM
hjic-103	82	9	:	:	PUNCT
hjic-103	82	10	nn	nn	PROPN
hjic-103	82	11	2	2	NUM
hjic-103	82	12	1	1	NUM
hjic-103	82	13	,	,	PUNCT
hjic-103	82	14	2	2	NUM
hjic-103	82	15	1	1	NUM
hjic-103	82	16	=	=	NOUN
hjic-103	82	17	=	=	NOUN
hjic-103	82	18	′	′	NOUN
hjic-103	82	19	ττ	ττ	NOUN
hjic-103	82	20	(	(	PUNCT
hjic-103	82	21	9	9	NUM
hjic-103	82	22	)	)	PUNCT
hjic-103	82	23	throughout	throughout	ADP
hjic-103	82	24	this	this	DET
hjic-103	82	25	work	work	NOUN
hjic-103	82	26	discrete	discrete	ADJ
hjic-103	82	27	recombination	recombination	NOUN
hjic-103	82	28	of	of	ADP
hjic-103	82	29	the	the	DET
hjic-103	82	30	object	object	NOUN
hjic-103	82	31	variables	variable	NOUN
hjic-103	82	32	and	and	CCONJ
hjic-103	82	33	intermediate	intermediate	ADJ
hjic-103	82	34	recombination	recombination	NOUN
hjic-103	82	35	of	of	ADP
hjic-103	82	36	the	the	DET
hjic-103	82	37	strategy	strategy	NOUN
hjic-103	82	38	parameters	parameter	NOUN
hjic-103	82	39	were	be	AUX
hjic-103	82	40	used	use	VERB
hjic-103	82	41	:	:	PUNCT
hjic-103	82	42	(	(	PUNCT
hjic-103	82	43	)	)	PUNCT
hjic-103	82	44	2/	2/	NUM
hjic-103	82	45	or	or	CCONJ
hjic-103	82	46	,	,	PUNCT
hjic-103	82	47	,	,	PUNCT
hjic-103	82	48	,	,	PUNCT
hjic-103	82	49	,	,	PUNCT
hjic-103	82	50	,	,	PUNCT
hjic-103	82	51	,	,	PUNCT
hjic-103	82	52	imifij	imifij	NOUN
hjic-103	82	53	imifij	imifij	NOUN
hjic-103	82	54	xxx	xxx	X
hjic-103	82	55	σσσ	σσσ	X
hjic-103	83	1	+	+	NOUN
hjic-103	83	2	=	=	SYM
hjic-103	83	3	=	=	SYM
hjic-103	83	4	(	(	PUNCT
hjic-103	83	5	10	10	NUM
hjic-103	83	6	)	)	PUNCT
hjic-103	83	7	where	where	SCONJ
hjic-103	83	8	f	f	PROPN
hjic-103	83	9	and	and	CCONJ
hjic-103	83	10	m	m	PROPN
hjic-103	83	11	denotes	denote	VERB
hjic-103	83	12	the	the	DET
hjic-103	83	13	parents	parent	NOUN
hjic-103	83	14	,	,	PUNCT
hjic-103	83	15	j	j	PROPN
hjic-103	83	16	is	be	AUX
hjic-103	83	17	the	the	DET
hjic-103	83	18	index	index	NOUN
hjic-103	83	19	of	of	ADP
hjic-103	83	20	the	the	DET
hjic-103	83	21	new	new	ADJ
hjic-103	83	22	offspring	offspring	NOUN
hjic-103	83	23	.	.	PUNCT
hjic-103	84	1	application	application	NOUN
hjic-103	84	2	example	example	NOUN
hjic-103	84	3	the	the	DET
hjic-103	84	4	case	case	NOUN
hjic-103	84	5	study	study	NOUN
hjic-103	84	6	of	of	ADP
hjic-103	84	7	this	this	DET
hjic-103	84	8	paper	paper	NOUN
hjic-103	84	9	is	be	AUX
hjic-103	84	10	a	a	DET
hjic-103	84	11	fed	feed	VERB
hjic-103	84	12	-	-	PUNCT
hjic-103	84	13	batch	batch	NOUN
hjic-103	84	14	bioreactor	bioreactor	NOUN
hjic-103	84	15	with	with	ADP
hjic-103	84	16	non	non	ADJ
hjic-103	84	17	-	-	ADJ
hjic-103	84	18	monotonic	monotonic	ADJ
hjic-103	84	19	kinetics	kinetic	NOUN
hjic-103	85	1	[	[	X
hjic-103	85	2	6	6	NUM
hjic-103	85	3	]	]	PUNCT
hjic-103	85	4	.	.	PUNCT
hjic-103	86	1	the	the	DET
hjic-103	86	2	following	follow	VERB
hjic-103	86	3	equation	equation	NOUN
hjic-103	86	4	describes	describe	VERB
hjic-103	86	5	the	the	DET
hjic-103	86	6	mass	mass	ADJ
hjic-103	86	7	balance	balance	NOUN
hjic-103	86	8	of	of	ADP
hjic-103	86	9	the	the	DET
hjic-103	86	10	reactor	reactor	NOUN
hjic-103	86	11	:	:	PUNCT
hjic-103	86	12	u	u	NOUN
hjic-103	86	13	c	c	NOUN
hjic-103	86	14	x	x	SYM
hjic-103	86	15	x	x	SYM
hjic-103	86	16	v	v	NUM
hjic-103	86	17	x	x	X
hjic-103	86	18	s	s	NOUN
hjic-103	86	19	dt	dt	X
hjic-103	86	20	d	d	PROPN
hjic-103	86	21	ins	ins	PROPN
hjic-103	86	22	⎥	⎥	PROPN
hjic-103	86	23	⎥	⎥	PROPN
hjic-103	86	24	⎥	⎥	PROPN
hjic-103	87	1	⎦	⎦	NOUN
hjic-103	87	2	⎤	⎤	ADJ
hjic-103	87	3	⎢	⎢	NOUN
hjic-103	87	4	⎢	⎢	NOUN
hjic-103	87	5	⎢	⎢	NOUN
hjic-103	87	6	⎣	⎣	PROPN
hjic-103	87	7	⎡	⎡	NOUN
hjic-103	87	8	+	+	CCONJ
hjic-103	87	9	⎥	⎥	PROPN
hjic-103	87	10	⎥	⎥	PROPN
hjic-103	87	11	⎥	⎥	PROPN
hjic-103	88	1	⎦	⎦	NOUN
hjic-103	88	2	⎤	⎤	ADJ
hjic-103	88	3	⎢	⎢	NOUN
hjic-103	88	4	⎢	⎢	NOUN
hjic-103	88	5	⎢	⎢	NOUN
hjic-103	88	6	⎣	⎣	PROPN
hjic-103	88	7	⎡−	⎡−	PROPN
hjic-103	88	8	=	=	PROPN
hjic-103	88	9	⎥	⎥	PROPN
hjic-103	88	10	⎥	⎥	PROPN
hjic-103	88	11	⎥	⎥	PROPN
hjic-103	88	12	⎦	⎦	NOUN
hjic-103	88	13	⎤	⎤	ADJ
hjic-103	88	14	⎢	⎢	NOUN
hjic-103	88	15	⎢	⎢	NOUN
hjic-103	88	16	⎢	⎢	NOUN
hjic-103	88	17	⎣	⎣	PROPN
hjic-103	88	18	⎡	⎡	VERB
hjic-103	88	19	1	1	NUM
hjic-103	88	20	0	0	NUM
hjic-103	88	21	0	0	NUM
hjic-103	88	22	,	,	PUNCT
hjic-103	88	23	µ	µ	PROPN
hjic-103	88	24	σ	σ	X
hjic-103	88	25	(	(	PUNCT
hjic-103	88	26	11	11	NUM
hjic-103	88	27	)	)	PUNCT
hjic-103	88	28	where	where	SCONJ
hjic-103	88	29	s	s	NOUN
hjic-103	88	30	is	be	AUX
hjic-103	88	31	the	the	DET
hjic-103	88	32	mass	mass	NOUN
hjic-103	88	33	of	of	ADP
hjic-103	88	34	the	the	DET
hjic-103	88	35	substrate	substrate	NOUN
hjic-103	88	36	[	[	X
hjic-103	88	37	g	g	X
hjic-103	88	38	]	]	X
hjic-103	88	39	,	,	PUNCT
hjic-103	88	40	x	x	X
hjic-103	88	41	is	be	AUX
hjic-103	88	42	the	the	DET
hjic-103	88	43	mass	mass	NOUN
hjic-103	88	44	of	of	ADP
hjic-103	88	45	the	the	DET
hjic-103	88	46	micro	micro	NOUN
hjic-103	88	47	-	-	NOUN
hjic-103	88	48	organism	organism	NOUN
hjic-103	88	49	[	[	X
hjic-103	88	50	g	g	X
hjic-103	88	51	dw	dw	X
hjic-103	88	52	]	]	PUNCT
hjic-103	88	53	,	,	PUNCT
hjic-103	88	54	v	v	NOUN
hjic-103	88	55	is	be	AUX
hjic-103	88	56	the	the	DET
hjic-103	88	57	volume	volume	NOUN
hjic-103	88	58	[	[	X
hjic-103	88	59	l	l	X
hjic-103	88	60	]	]	X
hjic-103	88	61	,	,	PUNCT
hjic-103	88	62	u	u	NOUN
hjic-103	88	63	is	be	AUX
hjic-103	88	64	the	the	DET
hjic-103	88	65	inlet	inlet	ADJ
hjic-103	88	66	flowrate	flowrate	ADJ
hjic-103	88	67	[	[	X
hjic-103	88	68	l	l	X
hjic-103	88	69	/	/	SYM
hjic-103	88	70	h	h	NOUN
hjic-103	88	71	]	]	X
hjic-103	88	72	,	,	PUNCT
hjic-103	88	73	cs	cs	X
hjic-103	88	74	,	,	PUNCT
hjic-103	88	75	in	in	ADP
hjic-103	88	76	=	=	NUM
hjic-103	88	77	500	500	NUM
hjic-103	88	78	g	g	NOUN
hjic-103	88	79	/	/	SYM
hjic-103	88	80	l	l	NOUN
hjic-103	88	81	is	be	AUX
hjic-103	88	82	the	the	DET
hjic-103	88	83	substrate	substrate	NOUN
hjic-103	88	84	concentration	concentration	NOUN
hjic-103	88	85	in	in	ADP
hjic-103	88	86	the	the	DET
hjic-103	88	87	inlet	inlet	NOUN
hjic-103	88	88	feed	feed	NOUN
hjic-103	88	89	,	,	PUNCT
hjic-103	88	90	σ	σ	NOUN
hjic-103	88	91	=	=	SYM
hjic-103	88	92	µ/yx	µ/yx	PROPN
hjic-103	88	93	/	/	SYM
hjic-103	88	94	s	s	PART
hjic-103	89	1	+	+	NOUN
hjic-103	89	2	m	m	VERB
hjic-103	89	3	is	be	AUX
hjic-103	89	4	the	the	DET
hjic-103	89	5	specific	specific	ADJ
hjic-103	89	6	substrate	substrate	NOUN
hjic-103	89	7	consumption	consumption	NOUN
hjic-103	89	8	rate	rate	NOUN
hjic-103	89	9	,	,	PUNCT
hjic-103	89	10	where	where	SCONJ
hjic-103	89	11	,	,	PUNCT
hjic-103	89	12	yx	yx	PROPN
hjic-103	89	13	/	/	SYM
hjic-103	89	14	s	s	NOUN
hjic-103	89	15	=	=	SYM
hjic-103	89	16	0.47	0.47	NUM
hjic-103	89	17	g	g	PROPN
hjic-103	89	18	dw	dw	PROPN
hjic-103	89	19	/	/	SYM
hjic-103	89	20	g	g	PROPN
hjic-103	89	21	,	,	PUNCT
hjic-103	89	22	m	m	VERB
hjic-103	89	23	=	=	NOUN
hjic-103	89	24	0.29	0.29	NUM
hjic-103	89	25	g	g	NOUN
hjic-103	89	26	/	/	SYM
hjic-103	89	27	g	g	PROPN
hjic-103	89	28	dw	dw	PROPN
hjic-103	89	29	h	h	PROPN
hjic-103	89	30	,	,	PUNCT
hjic-103	89	31	while	while	SCONJ
hjic-103	89	32	µ	µ	X
hjic-103	89	33	[	[	PUNCT
hjic-103	89	34	1	1	NUM
hjic-103	89	35	/	/	SYM
hjic-103	89	36	h	h	NOUN
hjic-103	89	37	]	]	X
hjic-103	89	38	is	be	AUX
hjic-103	89	39	the	the	DET
hjic-103	89	40	kinetic	kinetic	ADJ
hjic-103	89	41	rate	rate	NOUN
hjic-103	89	42	.	.	PUNCT
hjic-103	90	1	the	the	DET
hjic-103	90	2	initial	initial	ADJ
hjic-103	90	3	conditions	condition	NOUN
hjic-103	90	4	:	:	PUNCT
hjic-103	90	5	s(t=0	s(t=0	ADJ
hjic-103	90	6	)	)	PUNCT
hjic-103	90	7	=	=	PUNCT
hjic-103	90	8	500	500	NUM
hjic-103	90	9	g	g	NOUN
hjic-103	90	10	,	,	PUNCT
hjic-103	90	11	x(t=0	x(t=0	ADJ
hjic-103	90	12	)	)	PUNCT
hjic-103	90	13	=	=	SYM
hjic-103	90	14	10.5	10.5	NUM
hjic-103	90	15	g	g	NOUN
hjic-103	90	16	dw	dw	PROPN
hjic-103	90	17	,	,	PUNCT
hjic-103	90	18	v(t=0	v(t=0	NUM
hjic-103	90	19	)	)	PUNCT
hjic-103	90	20	=	=	PUNCT
hjic-103	90	21	7	7	NUM
hjic-103	90	22	l.	l.	NOUN
hjic-103	90	23	the	the	DET
hjic-103	90	24	maximum	maximum	ADJ
hjic-103	90	25	volume	volume	NOUN
hjic-103	90	26	is	be	AUX
hjic-103	90	27	vmax	vmax	PROPN
hjic-103	90	28	=	=	PUNCT
hjic-103	90	29	10	10	NUM
hjic-103	90	30	l	l	NOUN
hjic-103	90	31	,	,	PUNCT
hjic-103	90	32	and	and	CCONJ
hjic-103	90	33	the	the	DET
hjic-103	90	34	maximum	maximum	ADJ
hjic-103	90	35	inlet	inlet	ADJ
hjic-103	90	36	flowrate	flowrate	ADJ
hjic-103	90	37	is	be	AUX
hjic-103	90	38	umax	umax	ADJ
hjic-103	90	39	=	=	NUM
hjic-103	90	40	0.3	0.3	NUM
hjic-103	90	41	l	l	NOUN
hjic-103	90	42	/	/	SYM
hjic-103	90	43	h.	h.	NOUN
hjic-103	90	44	two	two	NUM
hjic-103	90	45	kinetic	kinetic	ADJ
hjic-103	90	46	models	model	NOUN
hjic-103	90	47	of	of	ADP
hjic-103	90	48	the	the	DET
hjic-103	90	49	µ	µ	ADJ
hjic-103	90	50	kinetic	kinetic	ADJ
hjic-103	90	51	rate	rate	NOUN
hjic-103	90	52	were	be	AUX
hjic-103	90	53	considered	consider	VERB
hjic-103	90	54	:	:	PUNCT
hjic-103	90	55	monotonic	monotonic	ADJ
hjic-103	90	56	kinetic	kinetic	NOUN
hjic-103	90	57	(	(	PUNCT
hjic-103	90	58	monode	monode	PROPN
hjic-103	90	59	model	model	NOUN
hjic-103	90	60	)	)	PUNCT
hjic-103	90	61	:	:	PUNCT
hjic-103	90	62	(	(	PUNCT
hjic-103	90	63	)	)	PUNCT
hjic-103	90	64	ss	ss	PRON
hjic-103	90	65	s	s	NOUN
hjic-103	90	66	s	s	NOUN
hjic-103	90	67	m	m	VERB
hjic-103	90	68	ck	ck	PRON
hjic-103	90	69	cc	cc	PROPN
hjic-103	91	1	+	+	CCONJ
hjic-103	91	2	=	=	PUNCT
hjic-103	91	3	maxµµ	maxµµ	ADJ
hjic-103	91	4	(	(	PUNCT
hjic-103	91	5	12	12	NUM
hjic-103	91	6	)	)	PUNCT
hjic-103	91	7	non	non	ADJ
hjic-103	91	8	-	-	ADJ
hjic-103	91	9	monotonic	monotonic	ADJ
hjic-103	91	10	kinetic	kinetic	NOUN
hjic-103	91	11	(	(	PUNCT
hjic-103	91	12	haldane	haldane	NOUN
hjic-103	91	13	model	model	NOUN
hjic-103	91	14	)	)	PUNCT
hjic-103	91	15	:	:	PUNCT
hjic-103	91	16	(	(	PUNCT
hjic-103	91	17	)	)	PUNCT
hjic-103	91	18	issp	issp	PROPN
hjic-103	91	19	s	s	PART
hjic-103	91	20	ms	ms	PROPN
hjic-103	91	21	h	h	PROPN
hjic-103	91	22	kcck	kcck	PROPN
hjic-103	92	1	cc	cc	PROPN
hjic-103	93	1	/2++	/2++	PUNCT
hjic-103	94	1	=	=	NOUN
hjic-103	94	2	µµ	µµ	X
hjic-103	94	3	(	(	PUNCT
hjic-103	94	4	13	13	NUM
hjic-103	94	5	)	)	PUNCT
hjic-103	94	6	where	where	SCONJ
hjic-103	94	7	cs	cs	PROPN
hjic-103	94	8	=	=	SYM
hjic-103	94	9	s	s	PROPN
hjic-103	94	10	/	/	SYM
hjic-103	94	11	v.	v.	ADP
hjic-103	94	12	116	116	NUM
hjic-103	94	13	the	the	DET
hjic-103	94	14	majority	majority	NOUN
hjic-103	94	15	of	of	ADP
hjic-103	94	16	the	the	DET
hjic-103	94	17	oed	oed	PROPN
hjic-103	94	18	applications	application	NOUN
hjic-103	94	19	in	in	ADP
hjic-103	94	20	the	the	DET
hjic-103	94	21	literature	literature	NOUN
hjic-103	94	22	assume	assume	VERB
hjic-103	94	23	that	that	SCONJ
hjic-103	94	24	the	the	DET
hjic-103	94	25	structure	structure	NOUN
hjic-103	94	26	of	of	ADP
hjic-103	94	27	the	the	DET
hjic-103	94	28	model	model	NOUN
hjic-103	94	29	used	use	VERB
hjic-103	94	30	for	for	ADP
hjic-103	94	31	the	the	DET
hjic-103	94	32	design	design	NOUN
hjic-103	94	33	of	of	ADP
hjic-103	94	34	the	the	DET
hjic-103	94	35	experiment	experiment	NOUN
hjic-103	94	36	is	be	AUX
hjic-103	94	37	perfectly	perfectly	ADV
hjic-103	94	38	known	know	VERB
hjic-103	94	39	.	.	PUNCT
hjic-103	95	1	however	however	ADV
hjic-103	95	2	,	,	PUNCT
hjic-103	95	3	the	the	DET
hjic-103	95	4	model	model	NOUN
hjic-103	95	5	structure	structure	NOUN
hjic-103	95	6	is	be	AUX
hjic-103	95	7	often	often	ADV
hjic-103	95	8	inaccurate	inaccurate	ADJ
hjic-103	95	9	in	in	ADP
hjic-103	95	10	practice	practice	NOUN
hjic-103	95	11	.	.	PUNCT
hjic-103	96	1	for	for	ADP
hjic-103	96	2	example	example	NOUN
hjic-103	96	3	,	,	PUNCT
hjic-103	96	4	a	a	DET
hjic-103	96	5	simplified	simplified	ADJ
hjic-103	96	6	kinetic	kinetic	ADJ
hjic-103	96	7	model	model	NOUN
hjic-103	96	8	is	be	AUX
hjic-103	96	9	often	often	ADV
hjic-103	96	10	used	use	VERB
hjic-103	96	11	to	to	PART
hjic-103	96	12	describe	describe	VERB
hjic-103	96	13	the	the	DET
hjic-103	96	14	microbial	microbial	ADJ
hjic-103	96	15	dynamics	dynamic	NOUN
hjic-103	96	16	due	due	ADP
hjic-103	96	17	to	to	ADP
hjic-103	96	18	the	the	DET
hjic-103	96	19	lack	lack	NOUN
hjic-103	96	20	of	of	ADP
hjic-103	96	21	lack	lack	NOUN
hjic-103	96	22	of	of	ADP
hjic-103	96	23	accurate	accurate	ADJ
hjic-103	96	24	knowledge	knowledge	NOUN
hjic-103	96	25	of	of	ADP
hjic-103	96	26	the	the	DET
hjic-103	96	27	microbial	microbial	ADJ
hjic-103	96	28	dynamics	dynamic	NOUN
hjic-103	96	29	.	.	PUNCT
hjic-103	97	1	the	the	DET
hjic-103	97	2	purpose	purpose	NOUN
hjic-103	97	3	of	of	ADP
hjic-103	97	4	this	this	DET
hjic-103	97	5	study	study	NOUN
hjic-103	97	6	is	be	AUX
hjic-103	97	7	to	to	PART
hjic-103	97	8	illustrate	illustrate	VERB
hjic-103	97	9	the	the	DET
hjic-103	97	10	complications	complication	NOUN
hjic-103	97	11	that	that	PRON
hjic-103	97	12	may	may	AUX
hjic-103	97	13	arise	arise	VERB
hjic-103	97	14	from	from	ADP
hjic-103	97	15	this	this	DET
hjic-103	97	16	structural	structural	ADJ
hjic-103	97	17	uncertainty	uncertainty	NOUN
hjic-103	97	18	.	.	PUNCT
hjic-103	98	1	therefore	therefore	ADV
hjic-103	98	2	,	,	PUNCT
hjic-103	98	3	in	in	ADP
hjic-103	98	4	this	this	DET
hjic-103	98	5	simulated	simulate	VERB
hjic-103	98	6	example	example	NOUN
hjic-103	98	7	,	,	PUNCT
hjic-103	98	8	we	we	PRON
hjic-103	98	9	use	use	VERB
hjic-103	98	10	different	different	ADJ
hjic-103	98	11	kinetic	kinetic	ADJ
hjic-103	98	12	models	model	NOUN
hjic-103	98	13	during	during	ADP
hjic-103	98	14	the	the	DET
hjic-103	98	15	simulation	simulation	NOUN
hjic-103	98	16	of	of	ADP
hjic-103	98	17	the	the	DET
hjic-103	98	18	system	system	NOUN
hjic-103	98	19	(	(	PUNCT
hjic-103	98	20	considered	consider	VERB
hjic-103	98	21	as	as	ADP
hjic-103	98	22	a	a	DET
hjic-103	98	23	real	real	ADJ
hjic-103	98	24	,	,	PUNCT
hjic-103	98	25	unknown	unknown	ADJ
hjic-103	98	26	process	process	NOUN
hjic-103	98	27	)	)	PUNCT
hjic-103	98	28	and	and	CCONJ
hjic-103	98	29	in	in	ADP
hjic-103	98	30	the	the	DET
hjic-103	98	31	model	model	NOUN
hjic-103	98	32	itself	itself	PRON
hjic-103	98	33	.	.	PUNCT
hjic-103	99	1	it	it	PRON
hjic-103	99	2	is	be	AUX
hjic-103	99	3	assumed	assume	VERB
hjic-103	99	4	that	that	SCONJ
hjic-103	99	5	the	the	DET
hjic-103	99	6	‘	'	PUNCT
hjic-103	99	7	true	true	ADJ
hjic-103	99	8	’	'	PUNCT
hjic-103	99	9	process	process	NOUN
hjic-103	99	10	can	can	AUX
hjic-103	99	11	be	be	AUX
hjic-103	99	12	described	describe	VERB
hjic-103	99	13	by	by	ADP
hjic-103	99	14	a	a	DET
hjic-103	99	15	non	non	ADJ
hjic-103	99	16	-	-	ADJ
hjic-103	99	17	monotonic	monotonic	ADJ
hjic-103	99	18	kinetic	kinetic	ADJ
hjic-103	99	19	equation	equation	NOUN
hjic-103	99	20	(	(	PUNCT
hjic-103	99	21	eq	eq	NOUN
hjic-103	99	22	.	.	PROPN
hjic-103	99	23	13	13	NUM
hjic-103	99	24	)	)	PUNCT
hjic-103	99	25	,	,	PUNCT
hjic-103	99	26	while	while	SCONJ
hjic-103	99	27	the	the	DET
hjic-103	99	28	model	model	NOUN
hjic-103	99	29	contains	contain	VERB
hjic-103	99	30	a	a	DET
hjic-103	99	31	monotonic	monotonic	ADJ
hjic-103	99	32	kinetic	kinetic	NOUN
hjic-103	99	33	model	model	NOUN
hjic-103	99	34	(	(	PUNCT
hjic-103	99	35	eq	eq	NOUN
hjic-103	99	36	.	.	PROPN
hjic-103	99	37	12	12	NUM
hjic-103	99	38	)	)	PUNCT
hjic-103	99	39	.	.	PUNCT
hjic-103	100	1	the	the	DET
hjic-103	100	2	system	system	NOUN
hjic-103	100	3	was	be	AUX
hjic-103	100	4	simulated	simulate	VERB
hjic-103	100	5	with	with	ADP
hjic-103	100	6	µm	µm	NOUN
hjic-103	100	7	=	=	SYM
hjic-103	100	8	0.1	0.1	NUM
hjic-103	100	9	1	1	NUM
hjic-103	100	10	/	/	SYM
hjic-103	100	11	h	h	NOUN
hjic-103	100	12	,	,	PUNCT
hjic-103	100	13	kp	kp	PROPN
hjic-103	100	14	=	=	NOUN
hjic-103	100	15	1	1	NUM
hjic-103	100	16	g	g	NOUN
hjic-103	100	17	/	/	SYM
hjic-103	100	18	l	l	NOUN
hjic-103	100	19	and	and	CCONJ
hjic-103	100	20	ki	ki	PROPN
hjic-103	100	21	=	=	NOUN
hjic-103	101	1	500	500	NUM
hjic-103	101	2	g	g	NOUN
hjic-103	101	3	/	/	SYM
hjic-103	101	4	l.	l.	NOUN
hjic-103	101	5	the	the	DET
hjic-103	101	6	goal	goal	NOUN
hjic-103	101	7	was	be	AUX
hjic-103	101	8	to	to	PART
hjic-103	101	9	find	find	VERB
hjic-103	101	10	the	the	DET
hjic-103	101	11	unknown	unknown	ADJ
hjic-103	101	12	parameters	parameter	NOUN
hjic-103	101	13	of	of	ADP
hjic-103	101	14	the	the	DET
hjic-103	101	15	model	model	NOUN
hjic-103	101	16	:	:	PUNCT
hjic-103	101	17	µmax	µmax	NOUN
hjic-103	101	18	and	and	CCONJ
hjic-103	101	19	ks	ks	PROPN
hjic-103	101	20	(	(	PUNCT
hjic-103	101	21	the	the	DET
hjic-103	101	22	other	other	ADJ
hjic-103	101	23	parameters	parameter	NOUN
hjic-103	101	24	were	be	AUX
hjic-103	101	25	assumed	assume	VERB
hjic-103	101	26	to	to	PART
hjic-103	101	27	be	be	AUX
hjic-103	101	28	accurately	accurately	ADV
hjic-103	101	29	known	know	VERB
hjic-103	101	30	)	)	PUNCT
hjic-103	101	31	.	.	PUNCT
hjic-103	102	1	the	the	DET
hjic-103	102	2	µmax	µmax	ADJ
hjic-103	102	3	and	and	CCONJ
hjic-103	102	4	ks	ks	NOUN
hjic-103	102	5	parameters	parameter	NOUN
hjic-103	102	6	were	be	AUX
hjic-103	102	7	estimated	estimate	VERB
hjic-103	102	8	by	by	ADP
hjic-103	102	9	optimization	optimization	NOUN
hjic-103	102	10	,	,	PUNCT
hjic-103	102	11	see	see	VERB
hjic-103	102	12	eq	eq	ADP
hjic-103	102	13	.	.	PROPN
hjic-103	102	14	2	2	NUM
hjic-103	102	15	and	and	CCONJ
hjic-103	102	16	3	3	NUM
hjic-103	102	17	.	.	PUNCT
hjic-103	102	18	because	because	SCONJ
hjic-103	102	19	the	the	DET
hjic-103	102	20	measurement	measurement	NOUN
hjic-103	102	21	of	of	ADP
hjic-103	102	22	the	the	DET
hjic-103	102	23	micro	micro	ADJ
hjic-103	102	24	-	-	NOUN
hjic-103	102	25	organism	organism	ADJ
hjic-103	102	26	concentration	concentration	NOUN
hjic-103	102	27	cx	cx	NOUN
hjic-103	102	28	is	be	AUX
hjic-103	102	29	quite	quite	ADV
hjic-103	102	30	difficult	difficult	ADJ
hjic-103	102	31	in	in	ADP
hjic-103	102	32	practice	practice	NOUN
hjic-103	102	33	,	,	PUNCT
hjic-103	102	34	it	it	PRON
hjic-103	102	35	was	be	AUX
hjic-103	102	36	assumed	assume	VERB
hjic-103	102	37	that	that	SCONJ
hjic-103	102	38	only	only	ADV
hjic-103	102	39	the	the	DET
hjic-103	102	40	substrate	substrate	NOUN
hjic-103	102	41	concentration	concentration	NOUN
hjic-103	102	42	cs	cs	PROPN
hjic-103	102	43	is	be	AUX
hjic-103	102	44	measured	measure	VERB
hjic-103	102	45	.	.	PUNCT
hjic-103	103	1	consequently	consequently	ADV
hjic-103	103	2	,	,	PUNCT
hjic-103	103	3	in	in	ADP
hjic-103	103	4	this	this	DET
hjic-103	103	5	application	application	NOUN
hjic-103	103	6	example	example	NOUN
hjic-103	103	7	,	,	PUNCT
hjic-103	103	8	the	the	DET
hjic-103	103	9	system	system	NOUN
hjic-103	103	10	output	output	NOUN
hjic-103	103	11	is	be	AUX
hjic-103	103	12	s~~	s~~	NOUN
hjic-103	103	13	=	=	PROPN
hjic-103	103	14	y	y	PROPN
hjic-103	104	1	[	[	X
hjic-103	104	2	g	g	X
hjic-103	104	3	]	]	X
hjic-103	104	4	,	,	PUNCT
hjic-103	104	5	which	which	PRON
hjic-103	104	6	was	be	AUX
hjic-103	104	7	generated	generate	VERB
hjic-103	104	8	by	by	ADP
hjic-103	104	9	the	the	DET
hjic-103	104	10	simulation	simulation	NOUN
hjic-103	104	11	of	of	ADP
hjic-103	104	12	equations	equation	NOUN
hjic-103	104	13	(	(	PUNCT
hjic-103	104	14	11	11	NUM
hjic-103	104	15	)	)	PUNCT
hjic-103	104	16	and	and	CCONJ
hjic-103	104	17	(	(	PUNCT
hjic-103	104	18	12	12	NUM
hjic-103	104	19	)	)	PUNCT
hjic-103	104	20	,	,	PUNCT
hjic-103	104	21	and	and	CCONJ
hjic-103	104	22	the	the	DET
hjic-103	104	23	model	model	NOUN
hjic-103	104	24	output	output	NOUN
hjic-103	104	25	is	be	AUX
hjic-103	104	26	y	y	PROPN
hjic-103	104	27	=	=	SYM
hjic-103	104	28	s	s	PART
hjic-103	105	1	[	[	X
hjic-103	105	2	g	g	X
hjic-103	105	3	]	]	X
hjic-103	105	4	,	,	PUNCT
hjic-103	105	5	which	which	PRON
hjic-103	105	6	it	it	PRON
hjic-103	105	7	was	be	AUX
hjic-103	105	8	calculated	calculate	VERB
hjic-103	105	9	with	with	ADP
hjic-103	105	10	the	the	DET
hjic-103	105	11	use	use	NOUN
hjic-103	105	12	of	of	ADP
hjic-103	105	13	equations	equation	NOUN
hjic-103	105	14	(	(	PUNCT
hjic-103	105	15	11	11	NUM
hjic-103	105	16	)	)	PUNCT
hjic-103	105	17	and	and	CCONJ
hjic-103	105	18	(	(	PUNCT
hjic-103	105	19	13	13	NUM
hjic-103	105	20	)	)	PUNCT
hjic-103	105	21	.	.	PUNCT
hjic-103	106	1	we	we	PRON
hjic-103	106	2	applied	apply	VERB
hjic-103	106	3	the	the	DET
hjic-103	106	4	iterative	iterative	NOUN
hjic-103	106	5	oed	oed	PROPN
hjic-103	106	6	methodology	methodology	NOUN
hjic-103	106	7	to	to	PART
hjic-103	106	8	design	design	VERB
hjic-103	106	9	the	the	DET
hjic-103	106	10	feeding	feeding	NOUN
hjic-103	106	11	profile	profile	NOUN
hjic-103	106	12	u(t	u(t	NOUN
hjic-103	106	13	)	)	PUNCT
hjic-103	106	14	with	with	ADP
hjic-103	106	15	e	e	NOUN
hjic-103	106	16	-	-	ADJ
hjic-103	106	17	optimal	optimal	ADJ
hjic-103	106	18	criterion	criterion	NOUN
hjic-103	106	19	equations	equation	NOUN
hjic-103	106	20	(	(	PUNCT
hjic-103	106	21	4	4	NUM
hjic-103	106	22	)	)	PUNCT
hjic-103	106	23	and	and	CCONJ
hjic-103	106	24	(	(	PUNCT
hjic-103	106	25	6	6	NUM
hjic-103	106	26	)	)	PUNCT
hjic-103	106	27	.	.	PUNCT
hjic-103	107	1	the	the	DET
hjic-103	107	2	d	d	ADJ
hjic-103	107	3	-	-	ADJ
hjic-103	107	4	optimal	optimal	ADJ
hjic-103	107	5	criterion	criterion	NOUN
hjic-103	107	6	was	be	AUX
hjic-103	107	7	not	not	PART
hjic-103	107	8	used	use	VERB
hjic-103	107	9	,	,	PUNCT
hjic-103	107	10	because	because	SCONJ
hjic-103	107	11	our	our	PRON
hjic-103	107	12	experience	experience	NOUN
hjic-103	107	13	showed	show	VERB
hjic-103	107	14	that	that	SCONJ
hjic-103	107	15	the	the	DET
hjic-103	107	16	eoptimality	eoptimality	NOUN
hjic-103	107	17	is	be	AUX
hjic-103	107	18	better	well	ADV
hjic-103	107	19	suited	suited	ADJ
hjic-103	107	20	for	for	ADP
hjic-103	107	21	this	this	DET
hjic-103	107	22	problem	problem	NOUN
hjic-103	107	23	.	.	PUNCT
hjic-103	108	1	because	because	SCONJ
hjic-103	108	2	the	the	DET
hjic-103	108	3	number	number	NOUN
hjic-103	108	4	of	of	ADP
hjic-103	108	5	experiments	experiment	NOUN
hjic-103	108	6	and	and	CCONJ
hjic-103	108	7	the	the	DET
hjic-103	108	8	length	length	NOUN
hjic-103	108	9	of	of	ADP
hjic-103	108	10	experiments	experiment	NOUN
hjic-103	108	11	are	be	AUX
hjic-103	108	12	limited	limit	VERB
hjic-103	108	13	in	in	ADP
hjic-103	108	14	practice	practice	NOUN
hjic-103	108	15	,	,	PUNCT
hjic-103	108	16	the	the	DET
hjic-103	108	17	number	number	NOUN
hjic-103	108	18	of	of	ADP
hjic-103	108	19	iterations	iteration	NOUN
hjic-103	108	20	was	be	AUX
hjic-103	108	21	limited	limit	VERB
hjic-103	108	22	to	to	ADP
hjic-103	108	23	3	3	NUM
hjic-103	108	24	and	and	CCONJ
hjic-103	108	25	the	the	DET
hjic-103	108	26	length	length	NOUN
hjic-103	108	27	of	of	ADP
hjic-103	108	28	one	one	NUM
hjic-103	108	29	experiment	experiment	NOUN
hjic-103	108	30	was	be	AUX
hjic-103	108	31	40	40	NUM
hjic-103	108	32	h.	h.	NOUN
hjic-103	109	1	the	the	DET
hjic-103	109	2	sample	sample	NOUN
hjic-103	109	3	time	time	NOUN
hjic-103	109	4	was	be	AUX
hjic-103	109	5	4	4	NUM
hjic-103	109	6	h.	h.	NOUN
hjic-103	109	7	in	in	ADP
hjic-103	109	8	this	this	DET
hjic-103	109	9	example	example	NOUN
hjic-103	109	10	,	,	PUNCT
hjic-103	109	11	three	three	NUM
hjic-103	109	12	methods	method	NOUN
hjic-103	109	13	were	be	AUX
hjic-103	109	14	examined	examine	VERB
hjic-103	109	15	:	:	PUNCT
hjic-103	109	16	method	method	NOUN
hjic-103	109	17	1	1	NUM
hjic-103	109	18	:	:	PUNCT
hjic-103	109	19	manually	manually	ADV
hjic-103	109	20	selected	select	VERB
hjic-103	109	21	input	input	NOUN
hjic-103	109	22	profiles	profile	NOUN
hjic-103	109	23	.	.	PUNCT
hjic-103	110	1	to	to	PART
hjic-103	110	2	compare	compare	VERB
hjic-103	110	3	and	and	CCONJ
hjic-103	110	4	analyze	analyze	VERB
hjic-103	110	5	the	the	DET
hjic-103	110	6	effectiveness	effectiveness	NOUN
hjic-103	110	7	of	of	ADP
hjic-103	110	8	oed	oed	PROPN
hjic-103	110	9	,	,	PUNCT
hjic-103	110	10	firstly	firstly	ADV
hjic-103	110	11	the	the	DET
hjic-103	110	12	parameters	parameter	NOUN
hjic-103	110	13	were	be	AUX
hjic-103	110	14	estimated	estimate	VERB
hjic-103	110	15	with	with	ADP
hjic-103	110	16	two	two	NUM
hjic-103	110	17	feeding	feeding	NOUN
hjic-103	110	18	profiles	profile	NOUN
hjic-103	110	19	.	.	PUNCT
hjic-103	111	1	the	the	DET
hjic-103	111	2	first	first	ADJ
hjic-103	111	3	profile	profile	NOUN
hjic-103	111	4	was	be	AUX
hjic-103	111	5	:	:	PUNCT
hjic-103	111	6	u(t	u(t	NOUN
hjic-103	111	7	)	)	PUNCT
hjic-103	111	8	=	=	PUNCT
hjic-103	112	1	0.077	0.077	NUM
hjic-103	112	2	l	l	NOUN
hjic-103	112	3	/	/	SYM
hjic-103	112	4	h	h	PROPN
hjic-103	112	5	,	,	PUNCT
hjic-103	112	6	0	0	NUM
hjic-103	112	7	h	h	NOUN
hjic-103	112	8	<	<	X
hjic-103	112	9	t	t	X
hjic-103	112	10	<	<	X
hjic-103	112	11	40	40	NUM
hjic-103	112	12	h	h	NOUN
hjic-103	112	13	,	,	PUNCT
hjic-103	112	14	while	while	SCONJ
hjic-103	112	15	the	the	DET
hjic-103	112	16	second	second	ADJ
hjic-103	112	17	profile	profile	NOUN
hjic-103	112	18	was	be	AUX
hjic-103	112	19	:	:	PUNCT
hjic-103	112	20	u(t	u(t	NOUN
hjic-103	112	21	)	)	PUNCT
hjic-103	112	22	=	=	SYM
hjic-103	112	23	0	0	NUM
hjic-103	113	1	l	l	NOUN
hjic-103	113	2	/	/	SYM
hjic-103	113	3	h	h	PROPN
hjic-103	113	4	,	,	PUNCT
hjic-103	113	5	t	t	X
hjic-103	113	6	<	<	X
hjic-103	113	7	20	20	NUM
hjic-103	113	8	h	h	NOUN
hjic-103	113	9	and	and	CCONJ
hjic-103	113	10	u(t	u(t	NOUN
hjic-103	113	11	)	)	PUNCT
hjic-103	113	12	=	=	PUNCT
hjic-103	114	1	0.15	0.15	NUM
hjic-103	114	2	l	l	NOUN
hjic-103	114	3	/	/	SYM
hjic-103	114	4	h	h	NOUN
hjic-103	114	5	,	,	PUNCT
hjic-103	114	6	20	20	NUM
hjic-103	114	7	h	h	NOUN
hjic-103	114	8	<	<	X
hjic-103	114	9	t	t	X
hjic-103	114	10	<	<	X
hjic-103	114	11	40	40	NUM
hjic-103	114	12	h.	h.	NOUN
hjic-103	114	13	method	method	NOUN
hjic-103	114	14	2	2	NUM
hjic-103	114	15	:	:	PUNCT
hjic-103	114	16	iterative	iterative	NOUN
hjic-103	114	17	oed	oed	PROPN
hjic-103	114	18	with	with	ADP
hjic-103	114	19	nls	nls	NOUN
hjic-103	114	20	in	in	ADP
hjic-103	114	21	the	the	DET
hjic-103	114	22	parameter	parameter	NOUN
hjic-103	114	23	estimation	estimation	NOUN
hjic-103	114	24	step	step	NOUN
hjic-103	114	25	and	and	CCONJ
hjic-103	114	26	sqp	sqp	PROPN
hjic-103	114	27	in	in	ADP
hjic-103	114	28	the	the	DET
hjic-103	114	29	experiment	experiment	NOUN
hjic-103	114	30	design	design	NOUN
hjic-103	114	31	step	step	NOUN
hjic-103	114	32	.	.	PUNCT
hjic-103	115	1	method	method	NOUN
hjic-103	115	2	3	3	NUM
hjic-103	115	3	:	:	PUNCT
hjic-103	115	4	iterative	iterative	NOUN
hjic-103	115	5	oed	oed	NOUN
hjic-103	115	6	with	with	ADP
hjic-103	115	7	es	es	NOUN
hjic-103	115	8	in	in	ADP
hjic-103	115	9	both	both	PRON
hjic-103	115	10	of	of	ADP
hjic-103	115	11	the	the	DET
hjic-103	115	12	parameter	parameter	NOUN
hjic-103	115	13	estimation	estimation	NOUN
hjic-103	115	14	and	and	CCONJ
hjic-103	115	15	experiment	experiment	NOUN
hjic-103	115	16	design	design	NOUN
hjic-103	115	17	steps	step	NOUN
hjic-103	115	18	.	.	PUNCT
hjic-103	116	1	because	because	SCONJ
hjic-103	116	2	the	the	DET
hjic-103	116	3	result	result	NOUN
hjic-103	116	4	depends	depend	VERB
hjic-103	116	5	on	on	ADP
hjic-103	116	6	the	the	DET
hjic-103	116	7	initial	initial	ADJ
hjic-103	116	8	parameter	parameter	NOUN
hjic-103	116	9	estimation	estimation	NOUN
hjic-103	116	10	,	,	PUNCT
hjic-103	116	11	two	two	NUM
hjic-103	116	12	initial	initial	ADJ
hjic-103	116	13	estimations	estimation	NOUN
hjic-103	116	14	were	be	AUX
hjic-103	116	15	applied	apply	VERB
hjic-103	116	16	:	:	PUNCT
hjic-103	116	17	=	=	SYM
hjic-103	116	18	0.05	0.05	NUM
hjic-103	116	19	1	1	NUM
hjic-103	116	20	/	/	SYM
hjic-103	116	21	h	h	NOUN
hjic-103	116	22	,	,	PUNCT
hjic-103	116	23	=	=	NOUN
hjic-103	116	24	0.5	0.5	NUM
hjic-103	116	25	g	g	NOUN
hjic-103	116	26	/	/	SYM
hjic-103	116	27	l	l	NOUN
hjic-103	116	28	and	and	CCONJ
hjic-103	116	29	=	=	SYM
hjic-103	116	30	0.1	0.1	NUM
hjic-103	116	31	1	1	NUM
hjic-103	116	32	/	/	SYM
hjic-103	116	33	h	h	NOUN
hjic-103	116	34	,	,	PUNCT
hjic-103	116	35	=	=	NOUN
hjic-103	116	36	1	1	NUM
hjic-103	116	37	g	g	NOUN
hjic-103	116	38	/	/	SYM
hjic-103	116	39	l.	l.	PROPN
hjic-103	116	40	init	init	PROPN
hjic-103	116	41	maxµ	maxµ	PROPN
hjic-103	116	42	init	init	VERB
hjic-103	116	43	sk	sk	PROPN
hjic-103	116	44	init	init	PROPN
hjic-103	116	45	maxµ	maxµ	PROPN
hjic-103	116	46	init	init	VERB
hjic-103	116	47	sk	sk	INTJ
hjic-103	116	48	certainly	certainly	ADV
hjic-103	116	49	,	,	PUNCT
hjic-103	116	50	there	there	PRON
hjic-103	116	51	is	be	VERB
hjic-103	116	52	no	no	DET
hjic-103	116	53	‘	'	PUNCT
hjic-103	116	54	perfect	perfect	ADJ
hjic-103	116	55	’	'	PUNCT
hjic-103	116	56	solution	solution	NOUN
hjic-103	116	57	for	for	ADP
hjic-103	116	58	this	this	DET
hjic-103	116	59	problem	problem	NOUN
hjic-103	116	60	.	.	PUNCT
hjic-103	117	1	to	to	PART
hjic-103	117	2	analyze	analyze	VERB
hjic-103	117	3	the	the	DET
hjic-103	117	4	results	result	NOUN
hjic-103	117	5	,	,	PUNCT
hjic-103	117	6	the	the	DET
hjic-103	117	7	obtained	obtain	VERB
hjic-103	117	8	monotonic	monotonic	ADJ
hjic-103	117	9	µm(cs	µm(cs	NOUN
hjic-103	117	10	)	)	PUNCT
hjic-103	117	11	functions	function	NOUN
hjic-103	117	12	were	be	AUX
hjic-103	117	13	compared	compare	VERB
hjic-103	117	14	to	to	ADP
hjic-103	117	15	the	the	DET
hjic-103	117	16	‘	'	PUNCT
hjic-103	117	17	true	true	ADJ
hjic-103	117	18	’	'	PUNCT
hjic-103	117	19	nonmonotonic	nonmonotonic	ADJ
hjic-103	117	20	µh(cs	µh(cs	PROPN
hjic-103	117	21	)	)	PUNCT
hjic-103	117	22	function	function	NOUN
hjic-103	117	23	:	:	PUNCT
hjic-103	117	24	(	(	PUNCT
hjic-103	117	25	)	)	PUNCT
hjic-103	117	26	(	(	PUNCT
hjic-103	117	27	)	)	PUNCT
hjic-103	117	28	(	(	PUNCT
hjic-103	117	29	)	)	PUNCT
hjic-103	117	30	∫	∫	PROPN
hjic-103	118	1	=	=	PRON
hjic-103	118	2	−=	−=	VERB
hjic-103	118	3	50	50	NUM
hjic-103	118	4	0	0	NUM
hjic-103	118	5	2	2	NUM
hjic-103	118	6	d	d	SYM
hjic-103	118	7	50	50	NUM
hjic-103	118	8	1	1	NUM
hjic-103	118	9	sc	sc	NOUN
hjic-103	118	10	ss	ss	NOUN
hjic-103	118	11	h	h	PROPN
hjic-103	118	12	s	s	PART
hjic-103	118	13	m	m	PROPN
hjic-103	118	14	mse	mse	NOUN
hjic-103	118	15	ccce	ccce	NOUN
hjic-103	118	16	µµ	µµ	X
hjic-103	118	17	(	(	PUNCT
hjic-103	118	18	14	14	NUM
hjic-103	118	19	)	)	PUNCT
hjic-103	118	20	as	as	ADP
hjic-103	118	21	table	table	NOUN
hjic-103	118	22	1	1	NUM
hjic-103	118	23	and	and	CCONJ
hjic-103	118	24	fig	fig	NOUN
hjic-103	118	25	.	.	PUNCT
hjic-103	119	1	4	4	NUM
hjic-103	119	2	show	show	NOUN
hjic-103	119	3	,	,	PUNCT
hjic-103	119	4	method	method	NOUN
hjic-103	119	5	3	3	NUM
hjic-103	119	6	proved	prove	VERB
hjic-103	119	7	to	to	PART
hjic-103	119	8	be	be	AUX
hjic-103	119	9	the	the	DET
hjic-103	119	10	best	good	ADJ
hjic-103	119	11	,	,	PUNCT
hjic-103	119	12	it	it	PRON
hjic-103	119	13	found	find	VERB
hjic-103	119	14	relatively	relatively	ADV
hjic-103	119	15	good	good	ADJ
hjic-103	119	16	solutions	solution	NOUN
hjic-103	119	17	.	.	PUNCT
hjic-103	120	1	one	one	PRON
hjic-103	120	2	can	can	AUX
hjic-103	120	3	see	see	VERB
hjic-103	120	4	that	that	DET
hjic-103	120	5	method	method	NOUN
hjic-103	120	6	2	2	NUM
hjic-103	120	7	was	be	AUX
hjic-103	120	8	sensitive	sensitive	ADJ
hjic-103	120	9	to	to	ADP
hjic-103	120	10	the	the	DET
hjic-103	120	11	initial	initial	ADJ
hjic-103	120	12	parameter	parameter	NOUN
hjic-103	120	13	estimation	estimation	NOUN
hjic-103	120	14	.	.	PUNCT
hjic-103	121	1	if	if	SCONJ
hjic-103	121	2	the	the	DET
hjic-103	121	3	initial	initial	ADJ
hjic-103	121	4	parameter	parameter	NOUN
hjic-103	121	5	were	be	AUX
hjic-103	121	6	=	=	NOUN
hjic-103	121	7	0.05	0.05	NUM
hjic-103	121	8	1	1	NUM
hjic-103	121	9	/	/	SYM
hjic-103	121	10	h	h	NOUN
hjic-103	121	11	,	,	PUNCT
hjic-103	121	12	=	=	NOUN
hjic-103	121	13	0.5	0.5	NUM
hjic-103	121	14	g	g	NOUN
hjic-103	121	15	/	/	SYM
hjic-103	121	16	l	l	NOUN
hjic-103	121	17	this	this	DET
hjic-103	121	18	method	method	NOUN
hjic-103	121	19	got	get	AUX
hjic-103	121	20	stuck	stick	VERB
hjic-103	121	21	into	into	ADP
hjic-103	121	22	a	a	DET
hjic-103	121	23	local	local	ADJ
hjic-103	121	24	minima	minima	NOUN
hjic-103	121	25	and	and	CCONJ
hjic-103	121	26	resulted	result	VERB
hjic-103	121	27	in	in	ADP
hjic-103	121	28	rather	rather	ADV
hjic-103	121	29	wrong	wrong	ADJ
hjic-103	121	30	parameter	parameter	NOUN
hjic-103	121	31	values	value	NOUN
hjic-103	121	32	.	.	PUNCT
hjic-103	122	1	if	if	SCONJ
hjic-103	122	2	=	=	NOUN
hjic-103	122	3	0.1	0.1	NUM
hjic-103	122	4	1	1	NUM
hjic-103	122	5	/	/	SYM
hjic-103	122	6	h	h	NOUN
hjic-103	122	7	,	,	PUNCT
hjic-103	122	8	=	=	NOUN
hjic-103	122	9	1	1	NUM
hjic-103	122	10	g	g	NOUN
hjic-103	122	11	/	/	SYM
hjic-103	122	12	l	l	NOUN
hjic-103	122	13	initial	initial	ADJ
hjic-103	122	14	estimations	estimation	NOUN
hjic-103	122	15	were	be	AUX
hjic-103	122	16	used	use	VERB
hjic-103	122	17	,	,	PUNCT
hjic-103	122	18	method	method	NOUN
hjic-103	122	19	2	2	NUM
hjic-103	122	20	resulted	result	VERB
hjic-103	122	21	in	in	ADP
hjic-103	122	22	better	well	ADJ
hjic-103	122	23	solution	solution	NOUN
hjic-103	122	24	.	.	PUNCT
hjic-103	123	1	in	in	ADP
hjic-103	123	2	contrast	contrast	NOUN
hjic-103	123	3	,	,	PUNCT
hjic-103	123	4	method	method	NOUN
hjic-103	123	5	3	3	NUM
hjic-103	123	6	always	always	ADV
hjic-103	123	7	resulted	result	VERB
hjic-103	123	8	in	in	ADP
hjic-103	123	9	a	a	DET
hjic-103	123	10	relatively	relatively	ADV
hjic-103	123	11	good	good	ADJ
hjic-103	123	12	solution	solution	NOUN
hjic-103	123	13	independently	independently	ADV
hjic-103	123	14	the	the	DET
hjic-103	123	15	initial	initial	ADJ
hjic-103	123	16	parameter	parameter	NOUN
hjic-103	123	17	values	value	NOUN
hjic-103	123	18	.	.	PUNCT
hjic-103	124	1	because	because	SCONJ
hjic-103	124	2	es	es	PRON
hjic-103	124	3	is	be	AUX
hjic-103	124	4	a	a	DET
hjic-103	124	5	stochastic	stochastic	ADJ
hjic-103	124	6	optimization	optimization	NOUN
hjic-103	124	7	algorithm	algorithm	NOUN
hjic-103	124	8	six	six	NUM
hjic-103	124	9	independent	independent	ADJ
hjic-103	124	10	runs	run	NOUN
hjic-103	124	11	were	be	AUX
hjic-103	124	12	performed	perform	VERB
hjic-103	124	13	and	and	CCONJ
hjic-103	124	14	we	we	PRON
hjic-103	124	15	got	get	VERB
hjic-103	124	16	very	very	ADV
hjic-103	124	17	similar	similar	ADJ
hjic-103	124	18	parameters	parameter	NOUN
hjic-103	124	19	in	in	ADP
hjic-103	124	20	each	each	DET
hjic-103	124	21	case	case	NOUN
hjic-103	124	22	.	.	PUNCT
hjic-103	125	1	init	init	AUX
hjic-103	125	2	maxµ	maxµ	PROPN
hjic-103	125	3	init	init	VERB
hjic-103	125	4	sk	sk	PROPN
hjic-103	125	5	init	init	PROPN
hjic-103	125	6	maxµ	maxµ	PROPN
hjic-103	125	7	init	init	VERB
hjic-103	125	8	sk	sk	PROPN
hjic-103	125	9	fig	fig	NOUN
hjic-103	125	10	.	.	PUNCT
hjic-103	126	1	5	5	NUM
hjic-103	126	2	and	and	CCONJ
hjic-103	126	3	6	6	NUM
hjic-103	126	4	demonstrate	demonstrate	VERB
hjic-103	126	5	that	that	SCONJ
hjic-103	126	6	the	the	DET
hjic-103	126	7	obtained	obtain	VERB
hjic-103	126	8	models	model	NOUN
hjic-103	126	9	output	output	NOUN
hjic-103	126	10	(	(	PUNCT
hjic-103	126	11	by	by	ADP
hjic-103	126	12	method	method	NOUN
hjic-103	126	13	2	2	NUM
hjic-103	126	14	and	and	CCONJ
hjic-103	126	15	method	method	NOUN
hjic-103	126	16	3	3	NUM
hjic-103	126	17	)	)	PUNCT
hjic-103	126	18	and	and	CCONJ
hjic-103	126	19	the	the	DET
hjic-103	126	20	system	system	NOUN
hjic-103	126	21	output	output	NOUN
hjic-103	126	22	for	for	ADP
hjic-103	126	23	two	two	NUM
hjic-103	126	24	input	input	NOUN
hjic-103	126	25	profiles	profile	NOUN
hjic-103	126	26	.	.	PUNCT
hjic-103	127	1	one	one	PRON
hjic-103	127	2	can	can	AUX
hjic-103	127	3	see	see	VERB
hjic-103	127	4	that	that	SCONJ
hjic-103	127	5	the	the	DET
hjic-103	127	6	obtained	obtain	VERB
hjic-103	127	7	models	model	NOUN
hjic-103	127	8	have	have	VERB
hjic-103	127	9	relatively	relatively	ADV
hjic-103	127	10	good	good	ADJ
hjic-103	127	11	prediction	prediction	NOUN
hjic-103	127	12	capability	capability	NOUN
hjic-103	127	13	.	.	PUNCT
hjic-103	128	1	the	the	DET
hjic-103	128	2	results	result	NOUN
hjic-103	128	3	support	support	VERB
hjic-103	128	4	the	the	DET
hjic-103	128	5	conclusion	conclusion	NOUN
hjic-103	128	6	that	that	SCONJ
hjic-103	128	7	if	if	SCONJ
hjic-103	128	8	the	the	DET
hjic-103	128	9	model	model	NOUN
hjic-103	128	10	structure	structure	NOUN
hjic-103	128	11	is	be	AUX
hjic-103	128	12	not	not	PART
hjic-103	128	13	accurate	accurate	ADJ
hjic-103	128	14	,	,	PUNCT
hjic-103	128	15	the	the	DET
hjic-103	128	16	oed	oed	NOUN
hjic-103	128	17	with	with	ADP
hjic-103	128	18	evolutionary	evolutionary	ADJ
hjic-103	128	19	strategy	strategy	NOUN
hjic-103	128	20	can	can	AUX
hjic-103	128	21	result	result	VERB
hjic-103	128	22	in	in	ADP
hjic-103	128	23	more	more	ADJ
hjic-103	128	24	satisfactory	satisfactory	ADJ
hjic-103	128	25	parameter	parameter	NOUN
hjic-103	128	26	values	value	NOUN
hjic-103	128	27	than	than	ADP
hjic-103	128	28	the	the	DET
hjic-103	128	29	classical	classical	ADJ
hjic-103	128	30	oed	oed	NOUN
hjic-103	128	31	with	with	ADP
hjic-103	128	32	sequential	sequential	ADJ
hjic-103	128	33	quadratic	quadratic	ADJ
hjic-103	128	34	programming	programming	NOUN
hjic-103	128	35	and	and	CCONJ
hjic-103	128	36	nonlinear	nonlinear	ADJ
hjic-103	128	37	least	least	ADJ
hjic-103	128	38	squares	square	NOUN
hjic-103	128	39	algorithms	algorithm	NOUN
hjic-103	128	40	.	.	PUNCT
hjic-103	129	1	table	table	NOUN
hjic-103	129	2	1	1	NUM
hjic-103	129	3	estimated	estimate	VERB
hjic-103	129	4	parameters	parameter	NOUN
hjic-103	129	5	and	and	CCONJ
hjic-103	129	6	the	the	DET
hjic-103	129	7	cost	cost	NOUN
hjic-103	129	8	values	value	NOUN
hjic-103	129	9	of	of	ADP
hjic-103	129	10	the	the	DET
hjic-103	129	11	obtained	obtain	VERB
hjic-103	129	12	kinetic	kinetic	NOUN
hjic-103	129	13	functions	function	NOUN
hjic-103	129	14	(	(	PUNCT
hjic-103	129	15	µmax	µmax	X
hjic-103	129	16	,	,	PUNCT
hjic-103	129	17	ks	ks	NOUN
hjic-103	129	18	)	)	PUNCT
hjic-103	129	19	emse·106	emse·106	PROPN
hjic-103	129	20	(	(	PUNCT
hjic-103	129	21	0.0893	0.0893	NUM
hjic-103	129	22	,	,	PUNCT
hjic-103	129	23	0	0	NUM
hjic-103	129	24	)	)	PUNCT
hjic-103	129	25	98.0	98.0	NUM
hjic-103	129	26	method	method	NOUN
hjic-103	129	27	1	1	NUM
hjic-103	129	28	*	*	SYM
hjic-103	129	29	(	(	PUNCT
hjic-103	129	30	0.0893	0.0893	NUM
hjic-103	129	31	,	,	PUNCT
hjic-103	129	32	0	0	NUM
hjic-103	129	33	)	)	PUNCT
hjic-103	129	34	98.0	98.0	NUM
hjic-103	129	35	(	(	PUNCT
hjic-103	129	36	0.0881	0.0881	NUM
hjic-103	129	37	,	,	PUNCT
hjic-103	129	38	0	0	NUM
hjic-103	129	39	)	)	PUNCT
hjic-103	129	40	96.0	96.0	NUM
hjic-103	129	41	method	method	NOUN
hjic-103	129	42	2	2	NUM
hjic-103	129	43	*	*	SYM
hjic-103	129	44	(	(	PUNCT
hjic-103	129	45	0.0894	0.0894	NUM
hjic-103	129	46	,	,	PUNCT
hjic-103	129	47	1.02	1.02	NUM
hjic-103	129	48	)	)	PUNCT
hjic-103	129	49	35.0	35.0	NUM
hjic-103	129	50	method	method	NOUN
hjic-103	129	51	3	3	NUM
hjic-103	129	52	*	*	NOUN
hjic-103	129	53	*	*	PUNCT
hjic-103	129	54	(	(	PUNCT
hjic-103	129	55	0.0895	0.0895	NUM
hjic-103	129	56	,	,	PUNCT
hjic-103	129	57	0.416	0.416	NUM
hjic-103	129	58	)	)	PUNCT
hjic-103	129	59	15.6	15.6	NUM
hjic-103	129	60	*	*	NOUN
hjic-103	129	61	these	these	DET
hjic-103	129	62	methods	method	NOUN
hjic-103	129	63	were	be	AUX
hjic-103	129	64	initialized	initialize	VERB
hjic-103	129	65	with	with	ADP
hjic-103	129	66	two	two	NUM
hjic-103	129	67	parameter	parameter	NOUN
hjic-103	129	68	vectors	vector	NOUN
hjic-103	129	69	,	,	PUNCT
hjic-103	129	70	see	see	VERB
hjic-103	129	71	above	above	ADV
hjic-103	129	72	.	.	PUNCT
hjic-103	130	1	*	*	PUNCT
hjic-103	130	2	*	*	PUNCT
hjic-103	130	3	the	the	DET
hjic-103	130	4	means	mean	NOUN
hjic-103	130	5	of	of	ADP
hjic-103	130	6	the	the	DET
hjic-103	130	7	emse	emse	ADJ
hjic-103	130	8	values	value	NOUN
hjic-103	130	9	of	of	ADP
hjic-103	130	10	six	six	NUM
hjic-103	130	11	independent	independent	ADJ
hjic-103	130	12	runs	run	NOUN
hjic-103	130	13	.	.	PUNCT
hjic-103	131	1	two	two	NUM
hjic-103	131	2	initial	initial	ADJ
hjic-103	131	3	parameter	parameter	NOUN
hjic-103	131	4	vectors	vector	NOUN
hjic-103	131	5	were	be	AUX
hjic-103	131	6	used	use	VERB
hjic-103	131	7	for	for	ADP
hjic-103	131	8	3	3	NUM
hjic-103	131	9	-	-	SYM
hjic-103	131	10	3	3	NUM
hjic-103	131	11	runs	run	NOUN
hjic-103	131	12	.	.	PUNCT
hjic-103	132	1	0	0	NUM
hjic-103	133	1	10	10	NUM
hjic-103	133	2	20	20	NUM
hjic-103	133	3	30	30	NUM
hjic-103	133	4	40	40	NUM
hjic-103	133	5	50	50	NUM
hjic-103	133	6	0	0	NUM
hjic-103	133	7	0.05	0.05	NUM
hjic-103	133	8	0.1	0.1	NUM
hjic-103	133	9	µ	µ	PRON
hjic-103	133	10	[	[	X
hjic-103	133	11	1	1	NUM
hjic-103	133	12	/h	/h	NOUN
hjic-103	133	13	]	]	X
hjic-103	133	14	0	0	NUM
hjic-103	133	15	10	10	NUM
hjic-103	133	16	20	20	NUM
hjic-103	133	17	30	30	NUM
hjic-103	133	18	40	40	NUM
hjic-103	133	19	50	50	NUM
hjic-103	133	20	0	0	NUM
hjic-103	133	21	0.05	0.05	NUM
hjic-103	133	22	0.1	0.1	NUM
hjic-103	133	23	µ	µ	PRON
hjic-103	133	24	[	[	X
hjic-103	133	25	1	1	NUM
hjic-103	133	26	/h	/h	NOUN
hjic-103	133	27	]	]	X
hjic-103	133	28	0	0	NUM
hjic-103	133	29	10	10	NUM
hjic-103	133	30	20	20	NUM
hjic-103	133	31	30	30	NUM
hjic-103	133	32	40	40	NUM
hjic-103	133	33	50	50	NUM
hjic-103	133	34	0	0	NUM
hjic-103	133	35	0.05	0.05	NUM
hjic-103	133	36	0.1	0.1	NUM
hjic-103	133	37	cs	cs	PROPN
hjic-103	133	38	[	[	X
hjic-103	133	39	g	g	NOUN
hjic-103	133	40	/	/	SYM
hjic-103	133	41	l	l	NOUN
hjic-103	133	42	]	]	X
hjic-103	133	43	µ	µ	X
hjic-103	133	44	[	[	X
hjic-103	133	45	1	1	NUM
hjic-103	133	46	/h	/h	SYM
hjic-103	133	47	]	]	PUNCT
hjic-103	133	48	fig	fig	NOUN
hjic-103	133	49	.	.	PUNCT
hjic-103	134	1	4	4	NUM
hjic-103	134	2	the	the	DET
hjic-103	134	3	obtained	obtain	VERB
hjic-103	134	4	kinetic	kinetic	NOUN
hjic-103	134	5	functions	function	NOUN
hjic-103	134	6	vs.	vs.	ADP
hjic-103	134	7	the	the	DET
hjic-103	134	8	‘	'	PUNCT
hjic-103	134	9	true	true	ADJ
hjic-103	134	10	’	'	PUNCT
hjic-103	134	11	kinetic	kinetic	ADJ
hjic-103	134	12	top	top	NOUN
hjic-103	134	13	:	:	PUNCT
hjic-103	134	14	method	method	NOUN
hjic-103	134	15	1	1	NUM
hjic-103	134	16	,	,	PUNCT
hjic-103	134	17	middle	middle	ADJ
hjic-103	134	18	:	:	PUNCT
hjic-103	134	19	method	method	NOUN
hjic-103	134	20	2	2	NUM
hjic-103	134	21	,	,	PUNCT
hjic-103	134	22	bottom	bottom	NOUN
hjic-103	134	23	:	:	PUNCT
hjic-103	134	24	method	method	NOUN
hjic-103	134	25	3	3	NUM
hjic-103	134	26	solid	solid	ADJ
hjic-103	134	27	line	line	NOUN
hjic-103	134	28	:	:	PUNCT
hjic-103	134	29	‘	'	PUNCT
hjic-103	134	30	true	true	ADJ
hjic-103	134	31	’	'	PUNCT
hjic-103	134	32	kinetic	kinetic	NOUN
hjic-103	134	33	,	,	PUNCT
hjic-103	134	34	dashed	dash	VERB
hjic-103	134	35	line	line	NOUN
hjic-103	134	36	:	:	PUNCT
hjic-103	134	37	obtained	obtain	VERB
hjic-103	134	38	kinetic	kinetic	ADJ
hjic-103	134	39	117	117	NUM
hjic-103	134	40	0	0	NUM
hjic-103	134	41	10	10	NUM
hjic-103	134	42	20	20	NUM
hjic-103	134	43	30	30	NUM
hjic-103	134	44	40	40	NUM
hjic-103	134	45	0	0	NUM
hjic-103	134	46	0.2	0.2	NUM
hjic-103	134	47	0.4	0.4	NUM
hjic-103	134	48	u	u	NOUN
hjic-103	135	1	[	[	X
hjic-103	135	2	l	l	X
hjic-103	135	3	/h	/h	X
hjic-103	135	4	]	]	X
hjic-103	135	5	0	0	NUM
hjic-103	135	6	10	10	NUM
hjic-103	135	7	20	20	NUM
hjic-103	135	8	30	30	NUM
hjic-103	135	9	40	40	NUM
hjic-103	135	10	0	0	NUM
hjic-103	135	11	50	50	NUM
hjic-103	135	12	100	100	NUM
hjic-103	135	13	c	c	NOUN
hjic-103	135	14	s	s	PART
hjic-103	136	1	[	[	X
hjic-103	136	2	g	g	NOUN
hjic-103	136	3	/l	/l	PUNCT
hjic-103	136	4	]	]	PUNCT
hjic-103	136	5	0	0	NUM
hjic-103	137	1	10	10	NUM
hjic-103	137	2	20	20	NUM
hjic-103	137	3	30	30	NUM
hjic-103	137	4	40	40	NUM
hjic-103	137	5	0	0	NUM
hjic-103	137	6	20	20	NUM
hjic-103	137	7	40	40	NUM
hjic-103	137	8	c	c	NOUN
hjic-103	137	9	x	x	PUNCT
hjic-103	138	1	[	[	X
hjic-103	138	2	g	g	X
hjic-103	138	3	/l	/l	PUNCT
hjic-103	138	4	]	]	PUNCT
hjic-103	139	1	t	t	X
hjic-103	140	1	[	[	X
hjic-103	140	2	h	h	X
hjic-103	140	3	]	]	X
hjic-103	140	4	fig	fig	NOUN
hjic-103	140	5	.	.	PUNCT
hjic-103	141	1	5	5	NUM
hjic-103	142	1	the	the	DET
hjic-103	142	2	obtained	obtain	VERB
hjic-103	142	3	model	model	NOUN
hjic-103	142	4	output	output	NOUN
hjic-103	142	5	vs.	vs.	ADP
hjic-103	142	6	the	the	DET
hjic-103	142	7	‘	'	PUNCT
hjic-103	142	8	true	true	ADJ
hjic-103	142	9	’	'	PUNCT
hjic-103	142	10	solid	solid	ADJ
hjic-103	142	11	:	:	PUNCT
hjic-103	142	12	system	system	NOUN
hjic-103	142	13	,	,	PUNCT
hjic-103	142	14	dashed	dash	VERB
hjic-103	142	15	:	:	PUNCT
hjic-103	142	16	method	method	NOUN
hjic-103	142	17	2	2	NUM
hjic-103	142	18	dotted	dot	VERB
hjic-103	142	19	:	:	PUNCT
hjic-103	142	20	method	method	NOUN
hjic-103	142	21	3	3	NUM
hjic-103	142	22	0	0	NUM
hjic-103	142	23	10	10	NUM
hjic-103	142	24	20	20	NUM
hjic-103	142	25	30	30	NUM
hjic-103	142	26	40	40	NUM
hjic-103	142	27	0	0	NUM
hjic-103	142	28	0.2	0.2	NUM
hjic-103	142	29	0.4	0.4	NUM
hjic-103	142	30	u	u	NOUN
hjic-103	143	1	[	[	X
hjic-103	143	2	l	l	X
hjic-103	143	3	/h	/h	X
hjic-103	143	4	]	]	X
hjic-103	143	5	0	0	NUM
hjic-103	143	6	10	10	NUM
hjic-103	143	7	20	20	NUM
hjic-103	143	8	30	30	NUM
hjic-103	143	9	40	40	NUM
hjic-103	143	10	0	0	NUM
hjic-103	143	11	50	50	NUM
hjic-103	143	12	100	100	NUM
hjic-103	143	13	c	c	NOUN
hjic-103	143	14	s	s	PART
hjic-103	144	1	[	[	X
hjic-103	144	2	g	g	NOUN
hjic-103	144	3	/l	/l	PUNCT
hjic-103	144	4	]	]	PUNCT
hjic-103	144	5	0	0	NUM
hjic-103	145	1	10	10	NUM
hjic-103	145	2	20	20	NUM
hjic-103	145	3	30	30	NUM
hjic-103	145	4	40	40	NUM
hjic-103	145	5	0	0	NUM
hjic-103	145	6	20	20	NUM
hjic-103	145	7	40	40	NUM
hjic-103	145	8	c	c	NOUN
hjic-103	145	9	x	x	PUNCT
hjic-103	146	1	[	[	X
hjic-103	146	2	g	g	X
hjic-103	146	3	/l	/l	PUNCT
hjic-103	146	4	]	]	PUNCT
hjic-103	147	1	t	t	X
hjic-103	148	1	[	[	X
hjic-103	148	2	h	h	X
hjic-103	148	3	]	]	X
hjic-103	148	4	fig	fig	NOUN
hjic-103	148	5	.	.	PUNCT
hjic-103	149	1	6	6	NUM
hjic-103	149	2	the	the	DET
hjic-103	149	3	obtained	obtain	VERB
hjic-103	149	4	model	model	NOUN
hjic-103	149	5	output	output	NOUN
hjic-103	149	6	vs.	vs.	ADP
hjic-103	149	7	the	the	DET
hjic-103	149	8	‘	'	PUNCT
hjic-103	149	9	true	true	ADJ
hjic-103	149	10	’	'	PUNCT
hjic-103	149	11	solid	solid	ADJ
hjic-103	149	12	:	:	PUNCT
hjic-103	149	13	system	system	NOUN
hjic-103	149	14	,	,	PUNCT
hjic-103	149	15	dashed	dash	VERB
hjic-103	149	16	:	:	PUNCT
hjic-103	149	17	method	method	NOUN
hjic-103	149	18	2	2	NUM
hjic-103	149	19	dotted	dot	VERB
hjic-103	149	20	:	:	PUNCT
hjic-103	149	21	method	method	NOUN
hjic-103	149	22	3	3	NUM
hjic-103	149	23	conclusions	conclusion	NOUN
hjic-103	149	24	the	the	DET
hjic-103	149	25	estimation	estimation	NOUN
hjic-103	149	26	of	of	ADP
hjic-103	149	27	model	model	NOUN
hjic-103	149	28	parameters	parameter	NOUN
hjic-103	149	29	largely	largely	ADV
hjic-103	149	30	depends	depend	VERB
hjic-103	149	31	on	on	ADP
hjic-103	149	32	the	the	DET
hjic-103	149	33	information	information	NOUN
hjic-103	149	34	content	content	NOUN
hjic-103	149	35	of	of	ADP
hjic-103	149	36	the	the	DET
hjic-103	149	37	experimental	experimental	ADJ
hjic-103	149	38	data	datum	NOUN
hjic-103	149	39	presented	present	VERB
hjic-103	149	40	to	to	ADP
hjic-103	149	41	the	the	DET
hjic-103	149	42	parameter	parameter	NOUN
hjic-103	149	43	identification	identification	NOUN
hjic-103	149	44	algorithm	algorithm	NOUN
hjic-103	149	45	.	.	PUNCT
hjic-103	150	1	establishing	establish	VERB
hjic-103	150	2	optimal	optimal	ADJ
hjic-103	150	3	experiment	experiment	NOUN
hjic-103	150	4	design	design	NOUN
hjic-103	150	5	(	(	PUNCT
hjic-103	150	6	oed	oed	PROPN
hjic-103	150	7	)	)	PUNCT
hjic-103	150	8	can	can	AUX
hjic-103	150	9	maximise	maximise	VERB
hjic-103	150	10	the	the	DET
hjic-103	150	11	confidence	confidence	NOUN
hjic-103	150	12	on	on	ADP
hjic-103	150	13	the	the	DET
hjic-103	150	14	model	model	NOUN
hjic-103	150	15	parameter	parameter	NOUN
hjic-103	150	16	,	,	PUNCT
hjic-103	150	17	hereby	hereby	ADV
hjic-103	150	18	increasing	increase	VERB
hjic-103	150	19	the	the	DET
hjic-103	150	20	confidence	confidence	NOUN
hjic-103	150	21	on	on	ADP
hjic-103	150	22	the	the	DET
hjic-103	150	23	model	model	NOUN
hjic-103	150	24	prediction	prediction	NOUN
hjic-103	150	25	.	.	PUNCT
hjic-103	151	1	both	both	CCONJ
hjic-103	151	2	the	the	DET
hjic-103	151	3	parameter	parameter	NOUN
hjic-103	151	4	estimation	estimation	NOUN
hjic-103	151	5	and	and	CCONJ
hjic-103	151	6	experiment	experiment	NOUN
hjic-103	151	7	design	design	NOUN
hjic-103	151	8	tasks	task	NOUN
hjic-103	151	9	represent	represent	VERB
hjic-103	151	10	a	a	DET
hjic-103	151	11	complex	complex	ADJ
hjic-103	151	12	nonlinear	nonlinear	ADJ
hjic-103	151	13	optimization	optimization	NOUN
hjic-103	151	14	problem	problem	NOUN
hjic-103	151	15	,	,	PUNCT
hjic-103	151	16	hence	hence	ADV
hjic-103	151	17	the	the	DET
hjic-103	151	18	effectiveness	effectiveness	NOUN
hjic-103	151	19	of	of	ADP
hjic-103	151	20	the	the	DET
hjic-103	151	21	applied	apply	VERB
hjic-103	151	22	optimization	optimization	NOUN
hjic-103	151	23	algorithms	algorithm	NOUN
hjic-103	151	24	has	have	VERB
hjic-103	151	25	great	great	ADJ
hjic-103	151	26	influence	influence	NOUN
hjic-103	151	27	on	on	ADP
hjic-103	151	28	the	the	DET
hjic-103	151	29	performance	performance	NOUN
hjic-103	151	30	of	of	ADP
hjic-103	151	31	the	the	DET
hjic-103	151	32	whole	whole	ADJ
hjic-103	151	33	procedure	procedure	NOUN
hjic-103	151	34	.	.	PUNCT
hjic-103	152	1	this	this	DET
hjic-103	152	2	paper	paper	NOUN
hjic-103	152	3	proposes	propose	VERB
hjic-103	152	4	the	the	DET
hjic-103	152	5	application	application	NOUN
hjic-103	152	6	of	of	ADP
hjic-103	152	7	evolutionary	evolutionary	ADJ
hjic-103	152	8	strategy	strategy	NOUN
hjic-103	152	9	(	(	PUNCT
hjic-103	152	10	es	es	NOUN
hjic-103	152	11	)	)	PUNCT
hjic-103	152	12	for	for	ADP
hjic-103	152	13	this	this	DET
hjic-103	152	14	purpose	purpose	NOUN
hjic-103	152	15	.	.	PUNCT
hjic-103	153	1	es	es	X
hjic-103	153	2	is	be	AUX
hjic-103	153	3	a	a	DET
hjic-103	153	4	stochastic	stochastic	ADJ
hjic-103	153	5	optimization	optimization	NOUN
hjic-103	153	6	algorithm	algorithm	NOUN
hjic-103	153	7	that	that	PRON
hjic-103	153	8	uses	use	VERB
hjic-103	153	9	the	the	DET
hjic-103	153	10	model	model	NOUN
hjic-103	153	11	of	of	ADP
hjic-103	153	12	natural	natural	ADJ
hjic-103	153	13	selection	selection	NOUN
hjic-103	153	14	.	.	PUNCT
hjic-103	154	1	in	in	ADP
hjic-103	154	2	this	this	DET
hjic-103	154	3	paper	paper	NOUN
hjic-103	154	4	,	,	PUNCT
hjic-103	154	5	this	this	DET
hjic-103	154	6	es	es	NUM
hjic-103	154	7	based	base	VERB
hjic-103	154	8	oed	oed	PROPN
hjic-103	154	9	technique	technique	NOUN
hjic-103	154	10	has	have	AUX
hjic-103	154	11	been	be	AUX
hjic-103	154	12	applied	apply	VERB
hjic-103	154	13	for	for	ADP
hjic-103	154	14	fed	fed	NOUN
hjic-103	154	15	-	-	PUNCT
hjic-103	154	16	batch	batch	NOUN
hjic-103	154	17	biochemical	biochemical	ADJ
hjic-103	154	18	reactor	reactor	NOUN
hjic-103	154	19	.	.	PUNCT
hjic-103	155	1	one	one	NUM
hjic-103	155	2	of	of	ADP
hjic-103	155	3	the	the	DET
hjic-103	155	4	factors	factor	NOUN
hjic-103	155	5	affecting	affect	VERB
hjic-103	155	6	the	the	DET
hjic-103	155	7	modelling	modelling	NOUN
hjic-103	155	8	of	of	ADP
hjic-103	155	9	biochemical	biochemical	ADJ
hjic-103	155	10	systems	system	NOUN
hjic-103	155	11	is	be	AUX
hjic-103	155	12	that	that	DET
hjic-103	155	13	accurate	accurate	ADJ
hjic-103	155	14	description	description	NOUN
hjic-103	155	15	of	of	ADP
hjic-103	155	16	biochemical	biochemical	ADJ
hjic-103	155	17	reaction	reaction	NOUN
hjic-103	155	18	is	be	AUX
hjic-103	155	19	generally	generally	ADV
hjic-103	155	20	not	not	PART
hjic-103	155	21	available	available	ADJ
hjic-103	155	22	a	a	DET
hjic-103	155	23	priori	priori	ADV
hjic-103	155	24	.	.	PUNCT
hjic-103	156	1	hence	hence	ADV
hjic-103	156	2	,	,	PUNCT
hjic-103	156	3	this	this	DET
hjic-103	156	4	paper	paper	NOUN
hjic-103	156	5	addressed	address	VERB
hjic-103	156	6	the	the	DET
hjic-103	156	7	application	application	NOUN
hjic-103	156	8	of	of	ADP
hjic-103	156	9	oed	oed	PROPN
hjic-103	156	10	for	for	ADP
hjic-103	156	11	models	model	NOUN
hjic-103	156	12	that	that	PRON
hjic-103	156	13	have	have	VERB
hjic-103	156	14	inaccurate	inaccurate	ADJ
hjic-103	156	15	structure	structure	NOUN
hjic-103	156	16	.	.	PUNCT
hjic-103	157	1	we	we	PRON
hjic-103	157	2	illustrated	illustrate	VERB
hjic-103	157	3	that	that	SCONJ
hjic-103	157	4	although	although	SCONJ
hjic-103	157	5	the	the	DET
hjic-103	157	6	model	model	NOUN
hjic-103	157	7	structure	structure	NOUN
hjic-103	157	8	used	use	VERB
hjic-103	157	9	for	for	ADP
hjic-103	157	10	the	the	DET
hjic-103	157	11	design	design	NOUN
hjic-103	157	12	of	of	ADP
hjic-103	157	13	the	the	DET
hjic-103	157	14	experiments	experiment	NOUN
hjic-103	157	15	is	be	AUX
hjic-103	157	16	not	not	PART
hjic-103	157	17	perfectly	perfectly	ADV
hjic-103	157	18	known	know	VERB
hjic-103	157	19	,	,	PUNCT
hjic-103	157	20	oed	oed	PROPN
hjic-103	157	21	can	can	AUX
hjic-103	157	22	result	result	VERB
hjic-103	157	23	in	in	ADP
hjic-103	157	24	satisfactory	satisfactory	ADJ
hjic-103	157	25	parameter	parameter	NOUN
hjic-103	157	26	values	value	NOUN
hjic-103	157	27	.	.	PUNCT
hjic-103	158	1	our	our	PRON
hjic-103	158	2	results	result	NOUN
hjic-103	158	3	support	support	VERB
hjic-103	158	4	the	the	DET
hjic-103	158	5	conclusion	conclusion	NOUN
hjic-103	158	6	that	that	SCONJ
hjic-103	158	7	when	when	SCONJ
hjic-103	158	8	the	the	DET
hjic-103	158	9	model	model	NOUN
hjic-103	158	10	structure	structure	NOUN
hjic-103	158	11	is	be	AUX
hjic-103	158	12	not	not	PART
hjic-103	158	13	accurate	accurate	ADJ
hjic-103	158	14	,	,	PUNCT
hjic-103	158	15	the	the	DET
hjic-103	158	16	oed	oed	NOUN
hjic-103	158	17	with	with	ADP
hjic-103	158	18	evolutionary	evolutionary	ADJ
hjic-103	158	19	strategy	strategy	NOUN
hjic-103	158	20	can	can	AUX
hjic-103	158	21	result	result	VERB
hjic-103	158	22	in	in	ADP
hjic-103	158	23	more	more	ADJ
hjic-103	158	24	satisfactory	satisfactory	ADJ
hjic-103	158	25	parameter	parameter	NOUN
hjic-103	158	26	values	value	NOUN
hjic-103	158	27	than	than	ADP
hjic-103	158	28	the	the	DET
hjic-103	158	29	classical	classical	ADJ
hjic-103	158	30	oed	oed	NOUN
hjic-103	158	31	with	with	ADP
hjic-103	158	32	sequential	sequential	ADJ
hjic-103	158	33	quadratic	quadratic	ADJ
hjic-103	158	34	programming	programming	NOUN
hjic-103	158	35	and	and	CCONJ
hjic-103	158	36	nonlinear	nonlinear	ADJ
hjic-103	158	37	least	least	ADJ
hjic-103	158	38	squares	square	NOUN
hjic-103	158	39	algorithms	algorithm	NOUN
hjic-103	158	40	.	.	PUNCT
hjic-103	159	1	acknowledgements	acknowledgement	NOUN
hjic-103	159	2	the	the	DET
hjic-103	159	3	authors	author	NOUN
hjic-103	159	4	would	would	AUX
hjic-103	159	5	like	like	VERB
hjic-103	159	6	to	to	PART
hjic-103	159	7	acknowledge	acknowledge	VERB
hjic-103	159	8	the	the	DET
hjic-103	159	9	support	support	NOUN
hjic-103	159	10	of	of	ADP
hjic-103	159	11	the	the	DET
hjic-103	159	12	cooperative	cooperative	ADJ
hjic-103	159	13	research	research	NOUN
hjic-103	159	14	centre	centre	NOUN
hjic-103	159	15	(	(	PUNCT
hjic-103	159	16	vikkk	vikkk	PROPN
hjic-103	159	17	)	)	PUNCT
hjic-103	159	18	(	(	PUNCT
hjic-103	159	19	project	project	NOUN
hjic-103	159	20	2003	2003	NUM
hjic-103	159	21	-	-	SYM
hjic-103	159	22	i	i	PROPN
hjic-103	159	23	)	)	PUNCT
hjic-103	159	24	,	,	PUNCT
hjic-103	159	25	and	and	CCONJ
hjic-103	159	26	founding	founding	NOUN
hjic-103	159	27	of	of	ADP
hjic-103	159	28	the	the	DET
hjic-103	159	29	hungarian	hungarian	ADJ
hjic-103	159	30	research	research	NOUN
hjic-103	159	31	found	find	VERB
hjic-103	159	32	(	(	PUNCT
hjic-103	159	33	otka	otka	PROPN
hjic-103	159	34	t037600	t037600	NOUN
hjic-103	159	35	)	)	PUNCT
hjic-103	159	36	.	.	PUNCT
hjic-103	160	1	references	reference	NOUN
hjic-103	160	2	1	1	NUM
hjic-103	160	3	.	.	PUNCT
hjic-103	160	4	bernaerts	bernaert	NOUN
hjic-103	160	5	,	,	PUNCT
hjic-103	160	6	k.	k.	PROPN
hjic-103	160	7	,	,	PUNCT
hjic-103	160	8	servaes	servaes	PROPN
hjic-103	160	9	,	,	PUNCT
hjic-103	160	10	r.d	r.d	PROPN
hjic-103	160	11	.	.	PROPN
hjic-103	160	12	,	,	PUNCT
hjic-103	160	13	kooyman	kooyman	PROPN
hjic-103	160	14	,	,	PUNCT
hjic-103	160	15	s.	s.	PROPN
hjic-103	160	16	and	and	CCONJ
hjic-103	160	17	versyck	versyck	PROPN
hjic-103	160	18	,	,	PUNCT
hjic-103	160	19	k.j	k.j	PROPN
hjic-103	160	20	.	.	PROPN
hjic-103	161	1	and	and	CCONJ
hjic-103	161	2	van	van	PROPN
hjic-103	161	3	impe	impe	PROPN
hjic-103	161	4	,	,	PUNCT
hjic-103	161	5	j.f	j.f	PROPN
hjic-103	161	6	.	.	PROPN
hjic-103	161	7	:	:	PUNCT
hjic-103	161	8	optimal	optimal	ADJ
hjic-103	161	9	temperature	temperature	NOUN
hjic-103	161	10	design	design	NOUN
hjic-103	161	11	for	for	ADP
hjic-103	161	12	estimation	estimation	NOUN
hjic-103	161	13	of	of	ADP
hjic-103	161	14	the	the	DET
hjic-103	161	15	square	square	ADJ
hjic-103	161	16	root	root	NOUN
hjic-103	161	17	model	model	NOUN
hjic-103	161	18	parameters	parameter	NOUN
hjic-103	161	19	:	:	PUNCT
hjic-103	161	20	parameter	parameter	NOUN
hjic-103	161	21	accuracy	accuracy	NOUN
hjic-103	161	22	and	and	CCONJ
hjic-103	161	23	model	model	NOUN
hjic-103	161	24	validity	validity	NOUN
hjic-103	161	25	restrictions	restriction	NOUN
hjic-103	161	26	,	,	PUNCT
hjic-103	161	27	int	int	NOUN
hjic-103	161	28	.	.	PUNCT
hjic-103	162	1	jour	jour	PROPN
hjic-103	162	2	.	.	PROPN
hjic-103	162	3	of	of	ADP
hjic-103	162	4	food	food	NOUN
hjic-103	162	5	microbiology	microbiology	NOUN
hjic-103	162	6	,	,	PUNCT
hjic-103	162	7	2002	2002	NUM
hjic-103	162	8	,	,	PUNCT
hjic-103	162	9	73	73	NUM
hjic-103	162	10	,	,	PUNCT
hjic-103	162	11	145	145	NUM
hjic-103	162	12	-	-	SYM
hjic-103	162	13	157	157	NUM
hjic-103	162	14	2	2	NUM
hjic-103	162	15	.	.	X
hjic-103	162	16	asprey	asprey	PROPN
hjic-103	162	17	,	,	PUNCT
hjic-103	162	18	s.p	s.p	PROPN
hjic-103	162	19	.	.	PROPN
hjic-103	162	20	and	and	CCONJ
hjic-103	162	21	macchietto	macchietto	PROPN
hjic-103	162	22	,	,	PUNCT
hjic-103	162	23	s.	s.	PROPN
hjic-103	162	24	:	:	PUNCT
hjic-103	162	25	designing	design	VERB
hjic-103	162	26	robust	robust	ADJ
hjic-103	162	27	optimal	optimal	ADJ
hjic-103	162	28	dynamic	dynamic	ADJ
hjic-103	162	29	experiments	experiment	NOUN
hjic-103	162	30	,	,	PUNCT
hjic-103	162	31	journal	journal	NOUN
hjic-103	162	32	of	of	ADP
hjic-103	162	33	process	process	NOUN
hjic-103	162	34	control	control	NOUN
hjic-103	162	35	,	,	PUNCT
hjic-103	162	36	2002	2002	NUM
hjic-103	162	37	,	,	PUNCT
hjic-103	162	38	12	12	NUM
hjic-103	162	39	,	,	PUNCT
hjic-103	162	40	545	545	NUM
hjic-103	162	41	-	-	SYM
hjic-103	162	42	556	556	NUM
hjic-103	162	43	3	3	NUM
hjic-103	162	44	.	.	X
hjic-103	162	45	espie	espie	PROPN
hjic-103	162	46	,	,	PUNCT
hjic-103	162	47	d.m	d.m	PROPN
hjic-103	162	48	.	.	PROPN
hjic-103	162	49	and	and	CCONJ
hjic-103	162	50	macchietto	macchietto	PROPN
hjic-103	162	51	,	,	PUNCT
hjic-103	162	52	s.	s.	PROPN
hjic-103	162	53	:	:	PUNCT
hjic-103	162	54	the	the	DET
hjic-103	162	55	optimal	optimal	ADJ
hjic-103	162	56	designs	design	NOUN
hjic-103	162	57	of	of	ADP
hjic-103	162	58	dynamic	dynamic	ADJ
hjic-103	162	59	experiments	experiment	NOUN
hjic-103	162	60	,	,	PUNCT
hjic-103	162	61	aiche	aiche	PROPN
hjic-103	162	62	j.	j.	PROPN
hjic-103	162	63	,	,	PUNCT
hjic-103	162	64	1989	1989	NUM
hjic-103	162	65	,	,	PUNCT
hjic-103	162	66	35	35	NUM
hjic-103	162	67	,	,	PUNCT
hjic-103	162	68	223	223	NUM
hjic-103	162	69	-	-	SYM
hjic-103	162	70	229	229	NUM
hjic-103	162	71	4	4	NUM
hjic-103	162	72	.	.	PUNCT
hjic-103	163	1	versyck	versyck	PROPN
hjic-103	163	2	,	,	PUNCT
hjic-103	163	3	k.j	k.j	PROPN
hjic-103	163	4	.	.	PROPN
hjic-103	163	5	,	,	PUNCT
hjic-103	163	6	bernaerts	bernaert	NOUN
hjic-103	163	7	,	,	PUNCT
hjic-103	163	8	k.	k.	PROPN
hjic-103	163	9	,	,	PUNCT
hjic-103	163	10	geearerd	geearerd	PROPN
hjic-103	163	11	,	,	PUNCT
hjic-103	163	12	a.h	a.h	PROPN
hjic-103	163	13	.	.	PROPN
hjic-103	163	14	and	and	CCONJ
hjic-103	163	15	van	van	PROPN
hjic-103	163	16	impe	impe	PROPN
hjic-103	163	17	j.f	j.f	PROPN
hjic-103	163	18	.	.	PROPN
hjic-103	163	19	:	:	PUNCT
hjic-103	164	1	introducing	introduce	VERB
hjic-103	164	2	optimal	optimal	ADJ
hjic-103	164	3	experiment	experiment	NOUN
hjic-103	164	4	design	design	NOUN
hjic-103	164	5	in	in	ADP
hjic-103	164	6	predictive	predictive	ADJ
hjic-103	164	7	microbiology	microbiology	NOUN
hjic-103	164	8	:	:	PUNCT
hjic-103	164	9	a	a	DET
hjic-103	164	10	motivating	motivating	NOUN
hjic-103	164	11	example	example	NOUN
hjic-103	164	12	.	.	PUNCT
hjic-103	165	1	int	int	NOUN
hjic-103	165	2	.	.	PUNCT
hjic-103	166	1	journ	journ	PROPN
hjic-103	166	2	.	.	PUNCT
hjic-103	167	1	of	of	ADP
hjic-103	167	2	food	food	NOUN
hjic-103	167	3	microbiology	microbiology	NOUN
hjic-103	167	4	,	,	PUNCT
hjic-103	167	5	1999	1999	NUM
hjic-103	167	6	,	,	PUNCT
hjic-103	167	7	51(1	51(1	NUM
hjic-103	167	8	)	)	PUNCT
hjic-103	167	9	,	,	PUNCT
hjic-103	167	10	39	39	NUM
hjic-103	167	11	-	-	SYM
hjic-103	167	12	51	51	NUM
hjic-103	167	13	5	5	NUM
hjic-103	167	14	.	.	PUNCT
hjic-103	167	15	asprey	asprey	PROPN
hjic-103	167	16	,	,	PUNCT
hjic-103	167	17	s.p	s.p	PROPN
hjic-103	167	18	.	.	PROPN
hjic-103	167	19	and	and	CCONJ
hjic-103	167	20	macchietto	macchietto	PROPN
hjic-103	167	21	,	,	PUNCT
hjic-103	167	22	s.	s.	PROPN
hjic-103	167	23	:	:	PUNCT
hjic-103	167	24	statistical	statistical	ADJ
hjic-103	167	25	tools	tool	NOUN
hjic-103	167	26	for	for	ADP
hjic-103	167	27	optimal	optimal	ADJ
hjic-103	167	28	dynamic	dynamic	ADJ
hjic-103	167	29	model	model	NOUN
hjic-103	167	30	building	building	NOUN
hjic-103	167	31	,	,	PUNCT
hjic-103	167	32	comp	comp	PROPN
hjic-103	167	33	.	.	PUNCT
hjic-103	167	34	chem	chem	PROPN
hjic-103	167	35	.	.	PUNCT
hjic-103	168	1	eng	eng	PROPN
hjic-103	168	2	.	.	PROPN
hjic-103	168	3	,	,	PUNCT
hjic-103	168	4	2000	2000	NUM
hjic-103	168	5	,	,	PUNCT
hjic-103	168	6	24	24	NUM
hjic-103	168	7	,	,	PUNCT
hjic-103	168	8	1261	1261	NUM
hjic-103	168	9	-	-	SYM
hjic-103	168	10	1267	1267	NUM
hjic-103	168	11	6	6	NUM
hjic-103	168	12	.	.	PUNCT
hjic-103	169	1	smets	smet	NOUN
hjic-103	169	2	,	,	PUNCT
hjic-103	169	3	i.y.m	i.y.m	ADJ
hjic-103	169	4	,	,	PUNCT
hjic-103	169	5	versyck	versyck	PROPN
hjic-103	169	6	,	,	PUNCT
hjic-103	169	7	k.j.e	k.j.e	NOUN
hjic-103	169	8	and	and	CCONJ
hjic-103	169	9	van	van	PROPN
hjic-103	169	10	impe	impe	PROPN
hjic-103	169	11	,	,	PUNCT
hjic-103	169	12	j.f	j.f	PROPN
hjic-103	169	13	.	.	PROPN
hjic-103	169	14	:	:	PUNCT
hjic-103	169	15	optimal	optimal	ADJ
hjic-103	169	16	control	control	NOUN
hjic-103	169	17	theory	theory	NOUN
hjic-103	169	18	:	:	PUNCT
hjic-103	169	19	a	a	DET
hjic-103	169	20	generic	generic	ADJ
hjic-103	169	21	tool	tool	NOUN
hjic-103	169	22	for	for	ADP
hjic-103	169	23	identification	identification	NOUN
hjic-103	169	24	and	and	CCONJ
hjic-103	169	25	control	control	NOUN
hjic-103	169	26	of	of	ADP
hjic-103	169	27	(	(	PUNCT
hjic-103	169	28	bio-)chemical	bio-)chemical	ADJ
hjic-103	169	29	reactors	reactor	NOUN
hjic-103	169	30	,	,	PUNCT
hjic-103	169	31	annual	annual	ADJ
hjic-103	169	32	reviews	review	NOUN
hjic-103	169	33	in	in	ADP
hjic-103	169	34	control	control	NOUN
hjic-103	169	35	,	,	PUNCT
hjic-103	169	36	2002	2002	NUM
hjic-103	169	37	,	,	PUNCT
hjic-103	169	38	26	26	NUM
hjic-103	169	39	,	,	PUNCT
hjic-103	169	40	5773	5773	NUM
hjic-103	169	41	7	7	NUM
hjic-103	169	42	.	.	PUNCT
hjic-103	170	1	madár	madár	PROPN
hjic-103	170	2	,	,	PUNCT
hjic-103	170	3	j.	j.	PROPN
hjic-103	170	4	and	and	CCONJ
hjic-103	170	5	abonyi	abonyi	PROPN
hjic-103	170	6	,	,	PUNCT
hjic-103	170	7	j.	j.	PROPN
hjic-103	170	8	:	:	PUNCT
hjic-103	170	9	evolutionary	evolutionary	ADJ
hjic-103	170	10	algorithms	algorithm	NOUN
hjic-103	170	11	,	,	PUNCT
hjic-103	170	12	chapter	chapter	NOUN
hjic-103	170	13	2.10	2.10	NUM
hjic-103	170	14	.	.	PUNCT
hjic-103	171	1	in	in	ADP
hjic-103	171	2	instrument	instrument	NOUN
hjic-103	171	3	engineers	engineer	NOUN
hjic-103	171	4	'	'	PART
hjic-103	171	5	handbook	handbook	NOUN
hjic-103	171	6	,	,	PUNCT
hjic-103	171	7	4th	4th	ADJ
hjic-103	171	8	edition	edition	NOUN
hjic-103	171	9	,	,	PUNCT
hjic-103	171	10	volume	volume	NOUN
hjic-103	171	11	2	2	NUM
hjic-103	171	12	process	process	NOUN
hjic-103	171	13	control	control	NOUN
hjic-103	171	14	,	,	PUNCT
hjic-103	171	15	editor	editor	NOUN
hjic-103	171	16	:	:	PUNCT
hjic-103	171	17	b.	b.	PROPN
hjic-103	171	18	liptak	liptak	PROPN
hjic-103	171	19	,	,	PUNCT
hjic-103	171	20	crc	crc	NOUN
hjic-103	171	21	press	press	NOUN
hjic-103	171	22	,	,	PUNCT
hjic-103	171	23	2005	2005	NUM
hjic-103	171	24	8	8	NUM
hjic-103	171	25	.	.	PUNCT
hjic-103	171	26	schwefel	schwefel	PROPN
hjic-103	171	27	,	,	PUNCT
hjic-103	171	28	h.	h.	PROPN
hjic-103	171	29	:	:	PUNCT
hjic-103	171	30	numerical	numerical	ADJ
hjic-103	171	31	optimization	optimization	NOUN
hjic-103	171	32	of	of	ADP
hjic-103	171	33	computer	computer	NOUN
hjic-103	171	34	models	model	NOUN
hjic-103	171	35	.	.	PUNCT
hjic-103	172	1	wiley	wiley	PROPN
hjic-103	172	2	,	,	PUNCT
hjic-103	172	3	chichester	chichester	PROPN
hjic-103	172	4	,	,	PUNCT
hjic-103	172	5	1995	1995	NUM