Improving Wang–Mendel method performance in fuzzy rules generation using the fuzzy C-means clustering algorithm

The generation of fuzzy rules from samples for fuzzy modeling and control is significant. If samples contain noise and outliers, the Wang–Mendel (WM) method may lead to the extraction of invalid rules resulting in low confidence of the rules. The scale of the samples also affects the efficiency of t...

Full description

Saved in:

Bibliographic Details
Published in:	Neurocomputing (Amsterdam) Vol. 151; pp. 1293 - 1304
Main Authors:	Gou, Jin, Hou, Feng, Chen, Wenyu, Wang, Cheng, Luo, Wei
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 03.03.2015
Subjects:	Algorithms Clustering Completeness Construction Fuzzy Fuzzy C-means clustering algorithm Fuzzy logic Fuzzy set theory Fuzzy system Mathematical models Robustness Wang–Mendel method Fuzzy C-means clustering algorithm Fuzzy system Robustness Wang–Mendel method Completeness
ISSN:	0925-2312, 1872-8286
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	The generation of fuzzy rules from samples for fuzzy modeling and control is significant. If samples contain noise and outliers, the Wang–Mendel (WM) method may lead to the extraction of invalid rules resulting in low confidence of the rules. The scale of the samples also affects the efficiency of the WM method. Interaction among input variables can help the WM method achieve high completeness and robustness. The fuzzy C-means clustering (FCM) algorithm can reduce the scale of samples and undo noisy data to some degree. This paper aims to develop an FCM-based improved WM method that adopts a modified FCM algorithm to preprocess the original samples and compute the interaction among the samples. Then, the optimized samples are used to generate fuzzy rules, thereby building a complete rule set through extrapolation. Experimental results from two nonlinear functions and short-term load forecasting case study show that the proposed method not only has high completeness and robustness, but also ensures better prediction accuracy of the fuzzy system. •The WM method for fuzzy rule generation is lack of completeness and robustness.•Some problems of traditional FCM algorithm may be caused by sample set.•The FCM algorithm improved by affinity (AFCM algorithm) is proposed.•Improved WM method by AFCM algorithm for fuzzy rule generation is proposed.•The performance of the fuzzy system is enhanced by the proposed method.
AbstractList	The generation of fuzzy rules from samples for fuzzy modeling and control is significant. If samples contain noise and outliers, the Wang–Mendel (WM) method may lead to the extraction of invalid rules resulting in low confidence of the rules. The scale of the samples also affects the efficiency of the WM method. Interaction among input variables can help the WM method achieve high completeness and robustness. The fuzzy C-means clustering (FCM) algorithm can reduce the scale of samples and undo noisy data to some degree. This paper aims to develop an FCM-based improved WM method that adopts a modified FCM algorithm to preprocess the original samples and compute the interaction among the samples. Then, the optimized samples are used to generate fuzzy rules, thereby building a complete rule set through extrapolation. Experimental results from two nonlinear functions and short-term load forecasting case study show that the proposed method not only has high completeness and robustness, but also ensures better prediction accuracy of the fuzzy system. •The WM method for fuzzy rule generation is lack of completeness and robustness.•Some problems of traditional FCM algorithm may be caused by sample set.•The FCM algorithm improved by affinity (AFCM algorithm) is proposed.•Improved WM method by AFCM algorithm for fuzzy rule generation is proposed.•The performance of the fuzzy system is enhanced by the proposed method. The generation of fuzzy rules from samples for fuzzy modeling and control is significant. If samples contain noise and outliers, the Wang-Mendel (WM) method may lead to the extraction of invalid rules resulting in low confidence of the rules. The scale of the samples also affects the efficiency of the WM method. Interaction among input variables can help the WM method achieve high completeness and robustness. The fuzzy C-means clustering (FCM) algorithm can reduce the scale of samples and undo noisy data to some degree. This paper aims to develop an FCM-based improved WM method that adopts a modified FCM algorithm to preprocess the original samples and compute the interaction among the samples. Then, the optimized samples are used to generate fuzzy rules, thereby building a complete rule set through extrapolation. Experimental results from two nonlinear functions and short-term load forecasting case study show that the proposed method not only has high completeness and robustness, but also ensures better prediction accuracy of the fuzzy system.
Author	Wang, Cheng Luo, Wei Chen, Wenyu Hou, Feng Gou, Jin
Author_xml	– sequence: 1 givenname: Jin surname: Gou fullname: Gou, Jin email: goujin@gmail.com – sequence: 2 givenname: Feng surname: Hou fullname: Hou, Feng email: houfeng0205@gmail.com – sequence: 3 givenname: Wenyu surname: Chen fullname: Chen, Wenyu email: huiyu100@hqu.edn.cn – sequence: 4 givenname: Cheng surname: Wang fullname: Wang, Cheng email: wangcheng@hqu.edu.cn – sequence: 5 givenname: Wei surname: Luo fullname: Luo, Wei email: luowei.qinghua@gmail.com
BookMark	eNqFkL1u2zAURonCBeo4eYMOHLPI5Y9EkR0CFEbSBEjRJUVGgqGubBoSaZNUAHvKO_QN-ySR6kwdmukC937nA-45QzMfPCD0mZIlJVR82S49DDb0S0ZoOa6WpK4_oDmVNSskk2KG5kSxqmCcsk_oLKUtIbSmTM3R_q7fxfDs_Bo_Gr_-8_L7B_gGOtxD3oQG7yC2IfbGW8DO43Y4Hg84Dh0kvAYP0WQXPB7SVJA38BZYFT0Yn7DthpQhTkfTrUN0edOfo4-t6RJcvM0F-nVz_bC6Le5_fr9bfbsvLOcqF1ALUBWpQBnTUCpB8ko1zZNqwYiG2ZIIwSjnXIpaVEJwJYhkilBTlk-ytnyBLk-943_7AVLWvUsWus54CEPSVAglS0WJHKPlKWpjSClCq3fR9SYeNCV6Mqy3-mRYT4an7Wh4xL7-g1mX_wrJ0bjuPfjqBMPo4NlB1Mk6GDU3LoLNugnu_wWvc3Wedw
CitedBy_id	crossref_primary_10_1002_clen_201700162 crossref_primary_10_1016_j_ces_2017_09_048 crossref_primary_10_1007_s11432_018_9545_8 crossref_primary_10_1016_j_heliyon_2024_e37293 crossref_primary_10_1016_j_ins_2020_10_018 crossref_primary_10_1016_j_neucom_2015_03_068 crossref_primary_10_1016_j_bspc_2020_101953 crossref_primary_10_1038_s41598_023_47812_3 crossref_primary_10_1080_0952813X_2022_2135614 crossref_primary_10_3233_JIFS_17227 crossref_primary_10_1016_j_ifacol_2017_08_1504 crossref_primary_10_1016_j_neucom_2020_04_031 crossref_primary_10_1016_j_asoc_2024_112257 crossref_primary_10_1016_j_neucom_2020_02_124 crossref_primary_10_1088_1742_6596_3102_1_012044 crossref_primary_10_3233_JIFS_182518 crossref_primary_10_1002_cpe_6913 crossref_primary_10_1109_TFUZZ_2021_3098339 crossref_primary_10_1016_j_neucom_2022_09_053 crossref_primary_10_1038_s41598_024_75830_2 crossref_primary_10_1007_s10489_021_02421_3 crossref_primary_10_1016_j_epsr_2023_109967 crossref_primary_10_1016_j_neucom_2018_10_010 crossref_primary_10_1007_s00521_017_3181_7 crossref_primary_10_1049_joe_2019_0938 crossref_primary_10_1109_TCE_2025_3541440 crossref_primary_10_3233_JIFS_169166 crossref_primary_10_1016_j_neucom_2015_10_089 crossref_primary_10_1002_ima_22608 crossref_primary_10_1007_s40815_022_01329_5 crossref_primary_10_1016_j_eswa_2025_127342 crossref_primary_10_1016_j_ins_2020_06_064 crossref_primary_10_1080_17457300_2019_1690002 crossref_primary_10_1007_s00500_016_2226_7 crossref_primary_10_3233_JIFS_231050 crossref_primary_10_1016_j_jhydrol_2021_125967
Cites_doi	10.1016/j.camwa.2011.07.005 10.1109/ICIEA.2010.5514696 10.1109/TCE.2010.5681159 10.1109/TFUZZ.2004.836065 10.1080/01969727308546046 10.1109/CDC.2007.4434426 10.1007/s00500-011-0748-6 10.1016/j.neucom.2011.05.029 10.1109/91.928739 10.1109/21.199466 10.3724/SP.J.1004.2010.01337 10.1016/j.eswa.2010.05.085 10.1016/0031-3203(94)90134-1 10.1109/iFUZZY.2012.6409738 10.1109/ICSMC.2009.5346036 10.1109/3477.790443 10.1016/0895-7177(93)90202-A 10.1109/TFUZZ.2003.819839 10.1109/TSMC.1987.6499296 10.1016/0098-3004(84)90020-7 10.1016/j.ins.2012.06.021 10.1109/TFUZZ.2004.841738
ContentType	Journal Article
Copyright	2014 Elsevier B.V.
Copyright_xml	– notice: 2014 Elsevier B.V.
DBID	AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D
DOI	10.1016/j.neucom.2014.10.077
DatabaseName	CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional
DatabaseTitleList	Computer and Information Systems Abstracts
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	1872-8286
EndPage	1304
ExternalDocumentID	10_1016_j_neucom_2014_10_077 S0925231214014738
GroupedDBID	--- --K --M .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JM 9JN AABNK AACTN AADPK AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXLA AAXUO AAYFN ABBOA ABCQJ ABFNM ABJNI ABMAC ABYKQ ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGWIK AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W KOM LG9 M41 MO0 MOBAO N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SDF SDG SDP SES SPC SPCBC SSN SSV SSZ T5K ZMT ~G- 29N 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS FEDTE FGOYB HLZ HVGLF HZ~ R2- SBC SEW WUQ XPP ~HD 7SC 8FD JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c339t-e76e9505e9aad118e8359ddb9fea6d2c4066213338676566396082901a44b87c3
ISICitedReferencesCount	52
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000347753600035&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0925-2312
IngestDate	Sat Sep 27 23:20:44 EDT 2025 Sat Nov 29 07:54:43 EST 2025 Tue Nov 18 22:18:31 EST 2025 Fri Feb 23 02:28:29 EST 2024
IsPeerReviewed	true
IsScholarly	true
Keywords	Fuzzy C-means clustering algorithm Fuzzy system Robustness Wang–Mendel method Completeness
Language	English
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c339t-e76e9505e9aad118e8359ddb9fea6d2c4066213338676566396082901a44b87c3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
PQID	1669849108
PQPubID	23500
PageCount	12
ParticipantIDs	proquest_miscellaneous_1669849108 crossref_primary_10_1016_j_neucom_2014_10_077 crossref_citationtrail_10_1016_j_neucom_2014_10_077 elsevier_sciencedirect_doi_10_1016_j_neucom_2014_10_077
PublicationCentury	2000
PublicationDate	2015-03-03
PublicationDateYYYYMMDD	2015-03-03
PublicationDate_xml	– month: 03 year: 2015 text: 2015-03-03 day: 03
PublicationDecade	2010
PublicationTitle	Neurocomputing (Amsterdam)
PublicationYear	2015
Publisher	Elsevier B.V
Publisher_xml	– name: Elsevier B.V
References	Yang, Yuan, Yuan, Mao (bib14) 2010; 37 S. Chen, Y. Chen, W. Zhang, W. Yan, A method of extracting fuzzy control rules of structural vibration using fcm algorithm, in: 2010 The 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), Taichung, 2010, pp. 1550–1555. J. Casillas, O. Cordon, F. Herrera, Improving the Wang and Mendel׳s fuzzy rule learning method by inducing cooperation among rules, in: The 8th Information Processing and Management of Uncertainty in Knowledge-Based Systems Conference, Madrid, Spain, 2000, pp. 1682–1688. Wang, Chai (bib8) 2005; 20 Chakraborty, Pal (bib2) 2004; 15 Sulaiman, Isa (bib33) 2010; 56 Wang, Mendel (bib6) 1992; 22 Wang (bib12) 1994 Wang, Wang, Chai (bib9) 2010; 36 Guillaume (bib4) 2001; 9 Yu, Yang (bib30) 2005; 13 Yang (bib21) 1993; 18 Bezdek, Hathaway, Sabin, Tucker (bib28) 1987; 17 Yu, Yang, Lee (bib27) 2011; 62 Galende-Hernandez, Sainz-Palmero, Fuente-Aparicio (bib15) 2012; 16 Cox, O׳Hagan (bib11) 1998 Chen, Gou (bib17) 2012; 7 Hou, Gou (bib23) 2013; 45 (bib24) 2003 Tang, Chen, Hu, Yu (bib31) 2012; 217 Bezdek, Ehrlich, Full (bib20) 1984; 10 Ishibuchi, Nakashima, Murata (bib3) 1999; 29 C.C. Chang, C.J. Lin, LIBSVM: a library for support vector machines Dan, Zheng (bib32) 2012; 78 Y. Bai, T. Li, Robust fuzzy inference system for prediction of time series with outliers, in: 2012 International Conference on Fuzzy Theory and it׳s Applications (iFUZZY), Taichung, 2012, pp. 394–399. Wang (bib7) 2003; 11 Dunn (bib19) 1973; 3 Ishibuchi, Yamamoto (bib1) 2005; 13 Wei, Mendel (bib29) 1994; 27 W. Zhang, W. Liu, IFCM: fuzzy clustering for rule extraction of interval type-2 fuzzy logic system, in: 2007 46th IEEE Conference on Decision and Control, New Orleans, LA, 2007, pp. 5318–5322. Oliveira, Pedrycz (bib26) 2007 [Online], 2001. (bib25) 2003 E.R.R. Kato, O. Morandin Jr., M. Sgavioli, B.D. Muniz, Genetic tuning for improving Wang and Mendel׳s fuzzy database, in: IEEE International Conference on Systems, Man and Cybernetics, 2009, SMC 2009, San Antonio, TX, 2009, pp. 1015–1020. Zhu (bib18) 1992; 6 Hou (10.1016/j.neucom.2014.10.077_bib23) 2013; 45 Zhu (10.1016/j.neucom.2014.10.077_bib18) 1992; 6 Dunn (10.1016/j.neucom.2014.10.077_bib19) 1973; 3 Wang (10.1016/j.neucom.2014.10.077_bib7) 2003; 11 Galende-Hernandez (10.1016/j.neucom.2014.10.077_bib15) 2012; 16 Wang (10.1016/j.neucom.2014.10.077_bib6) 1992; 22 Yang (10.1016/j.neucom.2014.10.077_bib14) 2010; 37 Wang (10.1016/j.neucom.2014.10.077_bib9) 2010; 36 Wei (10.1016/j.neucom.2014.10.077_bib29) 1994; 27 10.1016/j.neucom.2014.10.077_bib10 Ishibuchi (10.1016/j.neucom.2014.10.077_bib1) 2005; 13 Guillaume (10.1016/j.neucom.2014.10.077_bib4) 2001; 9 Sulaiman (10.1016/j.neucom.2014.10.077_bib33) 2010; 56 Dan (10.1016/j.neucom.2014.10.077_bib32) 2012; 78 Yang (10.1016/j.neucom.2014.10.077_bib21) 1993; 18 Bezdek (10.1016/j.neucom.2014.10.077_bib28) 1987; 17 (10.1016/j.neucom.2014.10.077_bib24) 2003 Cox (10.1016/j.neucom.2014.10.077_bib11) 1998 Chen (10.1016/j.neucom.2014.10.077_bib17) 2012; 7 Yu (10.1016/j.neucom.2014.10.077_bib30) 2005; 13 Oliveira (10.1016/j.neucom.2014.10.077_bib26) 2007 Wang (10.1016/j.neucom.2014.10.077_bib12) 1994 10.1016/j.neucom.2014.10.077_bib22 Ishibuchi (10.1016/j.neucom.2014.10.077_bib3) 1999; 29 10.1016/j.neucom.2014.10.077_bib5 Chakraborty (10.1016/j.neucom.2014.10.077_bib2) 2004; 15 Yu (10.1016/j.neucom.2014.10.077_bib27) 2011; 62 Tang (10.1016/j.neucom.2014.10.077_bib31) 2012; 217 10.1016/j.neucom.2014.10.077_bib34 10.1016/j.neucom.2014.10.077_bib13 10.1016/j.neucom.2014.10.077_bib16 Bezdek (10.1016/j.neucom.2014.10.077_bib20) 1984; 10 Wang (10.1016/j.neucom.2014.10.077_bib8) 2005; 20 (10.1016/j.neucom.2014.10.077_bib25) 2003
References_xml	– volume: 3 start-page: 32 year: 1973 end-page: 57 ident: bib19 article-title: A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters publication-title: J. Cybern. – reference: Y. Bai, T. Li, Robust fuzzy inference system for prediction of time series with outliers, in: 2012 International Conference on Fuzzy Theory and it׳s Applications (iFUZZY), Taichung, 2012, pp. 394–399. – volume: 29 start-page: 601 year: 1999 end-page: 618 ident: bib3 article-title: Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems publication-title: IEEE Trans. Syst. Man Cybern.—Part B: Cybern. – year: 2003 ident: bib24 publication-title: Accuracy Improvements in Linguistic Fuzzy Modeling – volume: 15 start-page: 110 year: 2004 end-page: 123 ident: bib2 article-title: A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification publication-title: J. Spacecr. Rockets – reference: C.C. Chang, C.J. Lin, LIBSVM: a library for support vector machines 〈 – year: 1994 ident: bib12 article-title: Adaptive Fuzzy Systems and Control: Design and Stability Analysis – reference: J. Casillas, O. Cordon, F. Herrera, Improving the Wang and Mendel׳s fuzzy rule learning method by inducing cooperation among rules, in: The 8th Information Processing and Management of Uncertainty in Knowledge-Based Systems Conference, Madrid, Spain, 2000, pp. 1682–1688. – volume: 16 start-page: 451 year: 2012 end-page: 470 ident: bib15 article-title: Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection publication-title: Soft Comput. – volume: 20 start-page: 497 year: 2005 end-page: 503 ident: bib8 article-title: Mining fuzzy rules from data and its system implementation publication-title: J. Syst. Eng. – volume: 6 start-page: 57 year: 1992 end-page: 63 ident: bib18 article-title: Some common key problems and their dealing methods in the application of fuzzy mathematical methods publication-title: Fuzzy Syst. Math. – reference: W. Zhang, W. Liu, IFCM: fuzzy clustering for rule extraction of interval type-2 fuzzy logic system, in: 2007 46th IEEE Conference on Decision and Control, New Orleans, LA, 2007, pp. 5318–5322. – volume: 13 start-page: 164 year: 2005 end-page: 176 ident: bib30 article-title: Optimality test for generalized fcm and its application to parameter selection publication-title: IEEE Trans. Fuzzy Syst. – volume: 9 start-page: 426 year: 2001 end-page: 443 ident: bib4 article-title: Designing fuzzy inference systems from data publication-title: IEEE Trans. Fuzzy Syst. – volume: 10 start-page: 191 year: 1984 end-page: 203 ident: bib20 article-title: FCM publication-title: Comput. Geosci. – volume: 62 start-page: 2200 year: 2011 end-page: 2208 ident: bib27 article-title: Sample-weighted clustering methods publication-title: Comput. Math. Appl. – volume: 27 start-page: 1567 year: 1994 end-page: 1573 ident: bib29 article-title: Optimality tests for the fuzzy c-means algorithms publication-title: Pattern Recognit. – volume: 37 start-page: 8036 year: 2010 end-page: 8041 ident: bib14 article-title: An improved wm method based on pso for electric load forecasting publication-title: Expert Syst. Appl. – year: 2007 ident: bib26 article-title: Advances in Fuzzy Clustering and its Applications – volume: 17 start-page: 873 year: 1987 end-page: 877 ident: bib28 article-title: Convergence theory for fuzzy c-means publication-title: IEEE Trans. Syst. Man Cybern. – volume: 18 start-page: 1 year: 1993 end-page: 16 ident: bib21 article-title: A survey of fuzzy clustering publication-title: Math. Comput. Modell. – reference: 〉 [Online], 2001. – volume: 11 start-page: 768 year: 2003 end-page: 782 ident: bib7 article-title: The wm method completed publication-title: IEEE Trans. Fuzzy Syst. – volume: 45 start-page: 20 year: 2013 end-page: 27 ident: bib23 article-title: Fuzzy rule generation based on cowm and fcm algorithm publication-title: Int. J. Appl. Math. Stat. – volume: 56 start-page: 2702 year: 2010 end-page: 2710 ident: bib33 article-title: Denoising-based clustering algorithms for segmentation of low level salt-and-pepper noise-corrupted images publication-title: IEEE Trans. Consumer Electron. – year: 1998 ident: bib11 publication-title: The Fuzzy Systems Handbook, A Practitioner׳s Guide to Building, Using, and Maintaining Fuzzy Systems – reference: S. Chen, Y. Chen, W. Zhang, W. Yan, A method of extracting fuzzy control rules of structural vibration using fcm algorithm, in: 2010 The 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), Taichung, 2010, pp. 1550–1555. – reference: E.R.R. Kato, O. Morandin Jr., M. Sgavioli, B.D. Muniz, Genetic tuning for improving Wang and Mendel׳s fuzzy database, in: IEEE International Conference on Systems, Man and Cybernetics, 2009, SMC 2009, San Antonio, TX, 2009, pp. 1015–1020. – volume: 7 start-page: 2685 year: 2012 end-page: 2689 ident: bib17 article-title: Improved Wang–Mendel scheme based on cooperation among input variables publication-title: Int. Rev. Comput. Softw. – volume: 13 start-page: 428 year: 2005 end-page: 435 ident: bib1 article-title: Rule weight specification in fuzzy rule-based classification systems publication-title: IEEE Trans. Fuzzy Syst. – volume: 22 start-page: 1414 year: 1992 end-page: 1427 ident: bib6 article-title: Generating fuzzy rules by learning from examples publication-title: IEEE Trans. Syst. Man Cybern. – year: 2003 ident: bib25 publication-title: Interpretability Issues in Fuzzy Modeling – volume: 217 start-page: 21 year: 2012 end-page: 30 ident: bib31 article-title: Generation of a probabilistic fuzzy rule base by learning from examples publication-title: Inf. Sci. – volume: 36 start-page: 1337 year: 2010 end-page: 1342 ident: bib9 article-title: Extract of fuzzy rules with completeness and robustness publication-title: Acta Autom. Sin. – volume: 78 start-page: 48 year: 2012 end-page: 54 ident: bib32 article-title: Extracting linguistic rules from data sets using fuzzy logic and genetic algorithms publication-title: Neurocomputing – volume: 15 start-page: 110 issue: 1 year: 2004 ident: 10.1016/j.neucom.2014.10.077_bib2 article-title: A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification publication-title: J. Spacecr. Rockets – volume: 62 start-page: 2200 issue: 5 year: 2011 ident: 10.1016/j.neucom.2014.10.077_bib27 article-title: Sample-weighted clustering methods publication-title: Comput. Math. Appl. doi: 10.1016/j.camwa.2011.07.005 – ident: 10.1016/j.neucom.2014.10.077_bib22 doi: 10.1109/ICIEA.2010.5514696 – volume: 20 start-page: 497 issue: 5 year: 2005 ident: 10.1016/j.neucom.2014.10.077_bib8 article-title: Mining fuzzy rules from data and its system implementation publication-title: J. Syst. Eng. – volume: 56 start-page: 2702 issue: 4 year: 2010 ident: 10.1016/j.neucom.2014.10.077_bib33 article-title: Denoising-based clustering algorithms for segmentation of low level salt-and-pepper noise-corrupted images publication-title: IEEE Trans. Consumer Electron. doi: 10.1109/TCE.2010.5681159 – volume: 13 start-page: 164 issue: 1 year: 2005 ident: 10.1016/j.neucom.2014.10.077_bib30 article-title: Optimality test for generalized fcm and its application to parameter selection publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2004.836065 – volume: 3 start-page: 32 issue: 3 year: 1973 ident: 10.1016/j.neucom.2014.10.077_bib19 article-title: A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters publication-title: J. Cybern. doi: 10.1080/01969727308546046 – ident: 10.1016/j.neucom.2014.10.077_bib5 doi: 10.1109/CDC.2007.4434426 – volume: 16 start-page: 451 issue: 3 year: 2012 ident: 10.1016/j.neucom.2014.10.077_bib15 article-title: Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection publication-title: Soft Comput. doi: 10.1007/s00500-011-0748-6 – year: 1994 ident: 10.1016/j.neucom.2014.10.077_bib12 – volume: 78 start-page: 48 issue: 1 year: 2012 ident: 10.1016/j.neucom.2014.10.077_bib32 article-title: Extracting linguistic rules from data sets using fuzzy logic and genetic algorithms publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.05.029 – volume: 9 start-page: 426 issue: 3 year: 2001 ident: 10.1016/j.neucom.2014.10.077_bib4 article-title: Designing fuzzy inference systems from data publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/91.928739 – ident: 10.1016/j.neucom.2014.10.077_bib34 – volume: 22 start-page: 1414 issue: 6 year: 1992 ident: 10.1016/j.neucom.2014.10.077_bib6 article-title: Generating fuzzy rules by learning from examples publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/21.199466 – year: 1998 ident: 10.1016/j.neucom.2014.10.077_bib11 – volume: 36 start-page: 1337 issue: 9 year: 2010 ident: 10.1016/j.neucom.2014.10.077_bib9 article-title: Extract of fuzzy rules with completeness and robustness publication-title: Acta Autom. Sin. doi: 10.3724/SP.J.1004.2010.01337 – volume: 37 start-page: 8036 issue: 12 year: 2010 ident: 10.1016/j.neucom.2014.10.077_bib14 article-title: An improved wm method based on pso for electric load forecasting publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.05.085 – year: 2003 ident: 10.1016/j.neucom.2014.10.077_bib24 – volume: 27 start-page: 1567 issue: 11 year: 1994 ident: 10.1016/j.neucom.2014.10.077_bib29 article-title: Optimality tests for the fuzzy c-means algorithms publication-title: Pattern Recognit. doi: 10.1016/0031-3203(94)90134-1 – ident: 10.1016/j.neucom.2014.10.077_bib10 doi: 10.1109/iFUZZY.2012.6409738 – ident: 10.1016/j.neucom.2014.10.077_bib13 doi: 10.1109/ICSMC.2009.5346036 – year: 2003 ident: 10.1016/j.neucom.2014.10.077_bib25 – volume: 6 start-page: 57 issue: 2 year: 1992 ident: 10.1016/j.neucom.2014.10.077_bib18 article-title: Some common key problems and their dealing methods in the application of fuzzy mathematical methods publication-title: Fuzzy Syst. Math. – year: 2007 ident: 10.1016/j.neucom.2014.10.077_bib26 – volume: 29 start-page: 601 issue: 5 year: 1999 ident: 10.1016/j.neucom.2014.10.077_bib3 article-title: Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems publication-title: IEEE Trans. Syst. Man Cybern.—Part B: Cybern. doi: 10.1109/3477.790443 – volume: 18 start-page: 1 issue: 11 year: 1993 ident: 10.1016/j.neucom.2014.10.077_bib21 article-title: A survey of fuzzy clustering publication-title: Math. Comput. Modell. doi: 10.1016/0895-7177(93)90202-A – volume: 11 start-page: 768 issue: 6 year: 2003 ident: 10.1016/j.neucom.2014.10.077_bib7 article-title: The wm method completed publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2003.819839 – volume: 17 start-page: 873 issue: 5 year: 1987 ident: 10.1016/j.neucom.2014.10.077_bib28 article-title: Convergence theory for fuzzy c-means publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/TSMC.1987.6499296 – volume: 45 start-page: 20 issue: 15 year: 2013 ident: 10.1016/j.neucom.2014.10.077_bib23 article-title: Fuzzy rule generation based on cowm and fcm algorithm publication-title: Int. J. Appl. Math. Stat. – volume: 10 start-page: 191 issue: 2–3 year: 1984 ident: 10.1016/j.neucom.2014.10.077_bib20 article-title: FCM publication-title: Comput. Geosci. doi: 10.1016/0098-3004(84)90020-7 – ident: 10.1016/j.neucom.2014.10.077_bib16 – volume: 217 start-page: 21 issue: 25 year: 2012 ident: 10.1016/j.neucom.2014.10.077_bib31 article-title: Generation of a probabilistic fuzzy rule base by learning from examples publication-title: Inf. Sci. doi: 10.1016/j.ins.2012.06.021 – volume: 13 start-page: 428 issue: 4 year: 2005 ident: 10.1016/j.neucom.2014.10.077_bib1 article-title: Rule weight specification in fuzzy rule-based classification systems publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2004.841738 – volume: 7 start-page: 2685 issue: 5 year: 2012 ident: 10.1016/j.neucom.2014.10.077_bib17 article-title: Improved Wang–Mendel scheme based on cooperation among input variables publication-title: Int. Rev. Comput. Softw.
SSID	ssj0017129
Score	2.3370414
Snippet	The generation of fuzzy rules from samples for fuzzy modeling and control is significant. If samples contain noise and outliers, the Wang–Mendel (WM) method... The generation of fuzzy rules from samples for fuzzy modeling and control is significant. If samples contain noise and outliers, the Wang-Mendel (WM) method...
SourceID	proquest crossref elsevier
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	1293
SubjectTerms	Algorithms Clustering Completeness Construction Fuzzy Fuzzy C-means clustering algorithm Fuzzy logic Fuzzy set theory Fuzzy system Mathematical models Robustness Wang–Mendel method
Title	Improving Wang–Mendel method performance in fuzzy rules generation using the fuzzy C-means clustering algorithm
URI	https://dx.doi.org/10.1016/j.neucom.2014.10.077 https://www.proquest.com/docview/1669849108
Volume	151
WOSCitedRecordID	wos000347753600035&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-8286 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017129 issn: 0925-2312 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3dbtMwFLbKxgU3_CPGn4zEXZVpiZ3YvqymwUBiQmKI3kWJ45ROaTraZtp2xTvwCjwZT8I5tpN0DDRA4iaqXDux8n05Pj4-P4S8kAnjWuRlEMkCS5jl8M2xjAWSZ1FYgkKh48wWmxAHB3I8Vu8Gg29tLMxJJepanp6q4_8KNbQB2Bg6-xdwdzeFBvgNoMMVYIfrHwHfmwls5XvvzMDeoq278hWjMVtxFy8wrYdlc35-Nlw0lVliTWXjadEs22Aq12E3mBlY2oa6ajC_go1vrCbzxXT1abau5dqMH9rWi_CWiNEMBxTIvs7y8GreWA5NO37uuxZ4nZPe7cDJxY-mPmt667-TUPjnZN1uEcbWcYv1xrRLATXOKhnFATDECWjjZLIUkY12vyC0fZpaJ3ZRaVlbwmFd5r9cHpyl4mi7Ng36CsG0-Db69vlKMhcTb7_HyeBccA_KBZPXyGYkYgWyc3P0em_8pjutEmHkcjr6ybchmtaP8PKzfqcC_aQMWA3n8Da56bcmdOQodYcMTH2X3GrLflC_CtwjnzuGUQTi-5evjlvUcYuucYtOa2qpQy23aM8tarlFgVu-g-cW7blFO27dJx9e7h3u7ge-ckegGVOrwIjEKNCtjcqyArawBvR8VRS5Kk2WFJHmWHcgZIzJROCGgsE-2p7oZ5znUmj2gGzU89o8JFSJUJdFGeZxmfHMlNBTxzuGxbzgeaF2tghrX2aqfVp7rK5Spa3_4lHqIEgRAmwFCLZI0I06dmldrugvWpxSr5o6lTMFal0x8nkLawqSG4_jstrMm2UaJomSHNR1-eif7_6Y3Oi_rSdkY7VozFNyXZ-spsvFM8_THylQw2g
linkProvider	Elsevier
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Improving+Wang%E2%80%93Mendel+method+performance+in+fuzzy+rules+generation+using+the+fuzzy+C-means+clustering+algorithm&rft.jtitle=Neurocomputing+%28Amsterdam%29&rft.au=Gou%2C+Jin&rft.au=Hou%2C+Feng&rft.au=Chen%2C+Wenyu&rft.au=Wang%2C+Cheng&rft.date=2015-03-03&rft.pub=Elsevier+B.V&rft.issn=0925-2312&rft.eissn=1872-8286&rft.volume=151&rft.spage=1293&rft.epage=1304&rft_id=info:doi/10.1016%2Fj.neucom.2014.10.077&rft.externalDocID=S0925231214014738
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0925-2312&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0925-2312&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0925-2312&client=summon