Yarn Strength Modelling Using Fuzzy Expert System

Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Abhijit Majumdar, Ph.D. [verfasserIn] Anindya Ghosh, Ph.D. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2008

Übergeordnetes Werk:	In: Journal of Engineered Fibers and Fabrics - SAGE Publishing, 2019, 3(2008), 4, Seite 61-68
Übergeordnetes Werk:	volume:3 ; year:2008 ; number:4 ; pages:61-68

Links:	Link aufrufen Link aufrufen Journal toc

Katalog-ID:	DOAJ038675226

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ038675226
003	DE-627
005	20230503031109.0
007	cr uuu---uuuuu
008	230227s2008 xx \|\|\|\|\|o 00\| \|\|eng c
035			\|a (DE-627)DOAJ038675226
035			\|a (DE-599)DOAJ120e9beb8a364af8b3ca9a6c25100d1d
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TA401-492
050		0	\|a TP1-1185
050		0	\|a TP890-933
100	0		\|a Abhijit Majumdar, Ph.D. \|e verfasserin \|4 aut
245	1	0	\|a Yarn Strength Modelling Using Fuzzy Expert System
264		1	\|c 2008
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging.
653		0	\|a Materials of engineering and construction. Mechanics of materials
653		0	\|a Chemical technology
653		0	\|a Textile bleaching, dyeing, printing, etc.
700	0		\|a Anindya Ghosh, Ph.D. \|e verfasserin \|4 aut
773	0	8	\|i In \|t Journal of Engineered Fibers and Fabrics \|d SAGE Publishing, 2019 \|g 3(2008), 4, Seite 61-68 \|w (DE-627)548634734 \|w (DE-600)2393988-6 \|x 15589250 \|7 nnns
773	1	8	\|g volume:3 \|g year:2008 \|g number:4 \|g pages:61-68
856	4	0	\|u https://doaj.org/article/120e9beb8a364af8b3ca9a6c25100d1d \|z kostenfrei
856	4	0	\|u http://www.jeffjournal.org/papers/Volume3/3.4.7_Majumdar.pdf \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1558-9250 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2707
912			\|a GBV_ILN_2890
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 3 \|j 2008 \|e 4 \|h 61-68

Indexfelder

author_variant	m p a mp mpa g p a gp gpa
matchkey_str	article:15589250:2008----::anteghoelnuiguz
hierarchy_sort_str	2008
callnumber-subject-code	TA
publishDate	2008
allfields	(DE-627)DOAJ038675226 (DE-599)DOAJ120e9beb8a364af8b3ca9a6c25100d1d DE-627 ger DE-627 rakwb eng TA401-492 TP1-1185 TP890-933 Abhijit Majumdar, Ph.D. verfasserin aut Yarn Strength Modelling Using Fuzzy Expert System 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging. Materials of engineering and construction. Mechanics of materials Chemical technology Textile bleaching, dyeing, printing, etc. Anindya Ghosh, Ph.D. verfasserin aut In Journal of Engineered Fibers and Fabrics SAGE Publishing, 2019 3(2008), 4, Seite 61-68 (DE-627)548634734 (DE-600)2393988-6 15589250 nnns volume:3 year:2008 number:4 pages:61-68 https://doaj.org/article/120e9beb8a364af8b3ca9a6c25100d1d kostenfrei http://www.jeffjournal.org/papers/Volume3/3.4.7_Majumdar.pdf kostenfrei https://doaj.org/toc/1558-9250 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2707 GBV_ILN_2890 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2008 4 61-68
spelling	(DE-627)DOAJ038675226 (DE-599)DOAJ120e9beb8a364af8b3ca9a6c25100d1d DE-627 ger DE-627 rakwb eng TA401-492 TP1-1185 TP890-933 Abhijit Majumdar, Ph.D. verfasserin aut Yarn Strength Modelling Using Fuzzy Expert System 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging. Materials of engineering and construction. Mechanics of materials Chemical technology Textile bleaching, dyeing, printing, etc. Anindya Ghosh, Ph.D. verfasserin aut In Journal of Engineered Fibers and Fabrics SAGE Publishing, 2019 3(2008), 4, Seite 61-68 (DE-627)548634734 (DE-600)2393988-6 15589250 nnns volume:3 year:2008 number:4 pages:61-68 https://doaj.org/article/120e9beb8a364af8b3ca9a6c25100d1d kostenfrei http://www.jeffjournal.org/papers/Volume3/3.4.7_Majumdar.pdf kostenfrei https://doaj.org/toc/1558-9250 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2707 GBV_ILN_2890 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2008 4 61-68
allfields_unstemmed	(DE-627)DOAJ038675226 (DE-599)DOAJ120e9beb8a364af8b3ca9a6c25100d1d DE-627 ger DE-627 rakwb eng TA401-492 TP1-1185 TP890-933 Abhijit Majumdar, Ph.D. verfasserin aut Yarn Strength Modelling Using Fuzzy Expert System 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging. Materials of engineering and construction. Mechanics of materials Chemical technology Textile bleaching, dyeing, printing, etc. Anindya Ghosh, Ph.D. verfasserin aut In Journal of Engineered Fibers and Fabrics SAGE Publishing, 2019 3(2008), 4, Seite 61-68 (DE-627)548634734 (DE-600)2393988-6 15589250 nnns volume:3 year:2008 number:4 pages:61-68 https://doaj.org/article/120e9beb8a364af8b3ca9a6c25100d1d kostenfrei http://www.jeffjournal.org/papers/Volume3/3.4.7_Majumdar.pdf kostenfrei https://doaj.org/toc/1558-9250 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2707 GBV_ILN_2890 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2008 4 61-68
allfieldsGer	(DE-627)DOAJ038675226 (DE-599)DOAJ120e9beb8a364af8b3ca9a6c25100d1d DE-627 ger DE-627 rakwb eng TA401-492 TP1-1185 TP890-933 Abhijit Majumdar, Ph.D. verfasserin aut Yarn Strength Modelling Using Fuzzy Expert System 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging. Materials of engineering and construction. Mechanics of materials Chemical technology Textile bleaching, dyeing, printing, etc. Anindya Ghosh, Ph.D. verfasserin aut In Journal of Engineered Fibers and Fabrics SAGE Publishing, 2019 3(2008), 4, Seite 61-68 (DE-627)548634734 (DE-600)2393988-6 15589250 nnns volume:3 year:2008 number:4 pages:61-68 https://doaj.org/article/120e9beb8a364af8b3ca9a6c25100d1d kostenfrei http://www.jeffjournal.org/papers/Volume3/3.4.7_Majumdar.pdf kostenfrei https://doaj.org/toc/1558-9250 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2707 GBV_ILN_2890 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2008 4 61-68
allfieldsSound	(DE-627)DOAJ038675226 (DE-599)DOAJ120e9beb8a364af8b3ca9a6c25100d1d DE-627 ger DE-627 rakwb eng TA401-492 TP1-1185 TP890-933 Abhijit Majumdar, Ph.D. verfasserin aut Yarn Strength Modelling Using Fuzzy Expert System 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging. Materials of engineering and construction. Mechanics of materials Chemical technology Textile bleaching, dyeing, printing, etc. Anindya Ghosh, Ph.D. verfasserin aut In Journal of Engineered Fibers and Fabrics SAGE Publishing, 2019 3(2008), 4, Seite 61-68 (DE-627)548634734 (DE-600)2393988-6 15589250 nnns volume:3 year:2008 number:4 pages:61-68 https://doaj.org/article/120e9beb8a364af8b3ca9a6c25100d1d kostenfrei http://www.jeffjournal.org/papers/Volume3/3.4.7_Majumdar.pdf kostenfrei https://doaj.org/toc/1558-9250 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2707 GBV_ILN_2890 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2008 4 61-68
language	English
source	In Journal of Engineered Fibers and Fabrics 3(2008), 4, Seite 61-68 volume:3 year:2008 number:4 pages:61-68
sourceStr	In Journal of Engineered Fibers and Fabrics 3(2008), 4, Seite 61-68 volume:3 year:2008 number:4 pages:61-68
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Materials of engineering and construction. Mechanics of materials Chemical technology Textile bleaching, dyeing, printing, etc.
isfreeaccess_bool	true
container_title	Journal of Engineered Fibers and Fabrics
authorswithroles_txt_mv	Abhijit Majumdar, Ph.D. @@aut@@ Anindya Ghosh, Ph.D. @@aut@@
publishDateDaySort_date	2008-01-01T00:00:00Z
hierarchy_top_id	548634734
id	DOAJ038675226
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ038675226</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503031109.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2008 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ038675226</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ120e9beb8a364af8b3ca9a6c25100d1d</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TA401-492</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TP1-1185</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TP890-933</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Abhijit Majumdar, Ph.D.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Yarn Strength Modelling Using Fuzzy Expert System</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2008</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging.</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Materials of engineering and construction. Mechanics of materials</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Chemical technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Textile bleaching, dyeing, printing, etc.</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Anindya Ghosh, Ph.D.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Engineered Fibers and Fabrics</subfield><subfield code="d">SAGE Publishing, 2019</subfield><subfield code="g">3(2008), 4, Seite 61-68</subfield><subfield code="w">(DE-627)548634734</subfield><subfield code="w">(DE-600)2393988-6</subfield><subfield code="x">15589250</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:3</subfield><subfield code="g">year:2008</subfield><subfield code="g">number:4</subfield><subfield code="g">pages:61-68</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/120e9beb8a364af8b3ca9a6c25100d1d</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.jeffjournal.org/papers/Volume3/3.4.7_Majumdar.pdf</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1558-9250</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2707</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2890</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">3</subfield><subfield code="j">2008</subfield><subfield code="e">4</subfield><subfield code="h">61-68</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Abhijit Majumdar, Ph.D.
spellingShingle	Abhijit Majumdar, Ph.D. misc TA401-492 misc TP1-1185 misc TP890-933 misc Materials of engineering and construction. Mechanics of materials misc Chemical technology misc Textile bleaching, dyeing, printing, etc. Yarn Strength Modelling Using Fuzzy Expert System
authorStr	Abhijit Majumdar, Ph.D.
ppnlink_with_tag_str_mv	@@773@@(DE-627)548634734
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	DOAJ
remote_str	true
callnumber-label	TA401-492
illustrated	Not Illustrated
issn	15589250
topic_title	TA401-492 TP1-1185 TP890-933 Yarn Strength Modelling Using Fuzzy Expert System
topic	misc TA401-492 misc TP1-1185 misc TP890-933 misc Materials of engineering and construction. Mechanics of materials misc Chemical technology misc Textile bleaching, dyeing, printing, etc.
topic_unstemmed	misc TA401-492 misc TP1-1185 misc TP890-933 misc Materials of engineering and construction. Mechanics of materials misc Chemical technology misc Textile bleaching, dyeing, printing, etc.
topic_browse	misc TA401-492 misc TP1-1185 misc TP890-933 misc Materials of engineering and construction. Mechanics of materials misc Chemical technology misc Textile bleaching, dyeing, printing, etc.
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Journal of Engineered Fibers and Fabrics
hierarchy_parent_id	548634734
hierarchy_top_title	Journal of Engineered Fibers and Fabrics
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)548634734 (DE-600)2393988-6
title	Yarn Strength Modelling Using Fuzzy Expert System
ctrlnum	(DE-627)DOAJ038675226 (DE-599)DOAJ120e9beb8a364af8b3ca9a6c25100d1d
title_full	Yarn Strength Modelling Using Fuzzy Expert System
author_sort	Abhijit Majumdar, Ph.D.
journal	Journal of Engineered Fibers and Fabrics
journalStr	Journal of Engineered Fibers and Fabrics
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2008
contenttype_str_mv	txt
container_start_page	61
author_browse	Abhijit Majumdar, Ph.D. Anindya Ghosh, Ph.D.
container_volume	3
class	TA401-492 TP1-1185 TP890-933
format_se	Elektronische Aufsätze
author-letter	Abhijit Majumdar, Ph.D.
author2-role	verfasserin
title_sort	yarn strength modelling using fuzzy expert system
callnumber	TA401-492
title_auth	Yarn Strength Modelling Using Fuzzy Expert System
abstract	Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging.
abstractGer	Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging.
abstract_unstemmed	Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2707 GBV_ILN_2890 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	4
title_short	Yarn Strength Modelling Using Fuzzy Expert System
url	https://doaj.org/article/120e9beb8a364af8b3ca9a6c25100d1d http://www.jeffjournal.org/papers/Volume3/3.4.7_Majumdar.pdf https://doaj.org/toc/1558-9250
remote_bool	true
author2	Anindya Ghosh, Ph.D.
author2Str	Anindya Ghosh, Ph.D.
ppnlink	548634734
callnumber-subject	TA - General and Civil Engineering
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
callnumber-a	TA401-492
up_date	2024-07-03T19:15:50.925Z
_version_	1803586529843478528
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ038675226</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503031109.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2008 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ038675226</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ120e9beb8a364af8b3ca9a6c25100d1d</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TA401-492</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TP1-1185</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TP890-933</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Abhijit Majumdar, Ph.D.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Yarn Strength Modelling Using Fuzzy Expert System</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2008</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging.</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Materials of engineering and construction. Mechanics of materials</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Chemical technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Textile bleaching, dyeing, printing, etc.</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Anindya Ghosh, Ph.D.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Engineered Fibers and Fabrics</subfield><subfield code="d">SAGE Publishing, 2019</subfield><subfield code="g">3(2008), 4, Seite 61-68</subfield><subfield code="w">(DE-627)548634734</subfield><subfield code="w">(DE-600)2393988-6</subfield><subfield code="x">15589250</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:3</subfield><subfield code="g">year:2008</subfield><subfield code="g">number:4</subfield><subfield code="g">pages:61-68</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/120e9beb8a364af8b3ca9a6c25100d1d</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.jeffjournal.org/papers/Volume3/3.4.7_Majumdar.pdf</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1558-9250</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2707</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2890</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">3</subfield><subfield code="j">2008</subfield><subfield code="e">4</subfield><subfield code="h">61-68</subfield></datafield></record></collection>
score	7.399953

Nicht das Richtige dabei?

Schreiben Sie uns!

Yarn Strength Modelling Using Fuzzy Expert System

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?