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...

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Vydáno v:	Neurocomputing (Amsterdam) Ročník 151; s. 1293 - 1304
Hlavní autoři:	Gou, Jin, Hou, Feng, Chen, Wenyu, Wang, Cheng, Luo, Wei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 03.03.2015
Témata:	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
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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
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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...
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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
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