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Fuzzy Information & Engineering and Operations Research & Management pp 295–305Cite as

A Fuzzy Clustering Approach for TS Fuzzy Model Identification

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 211))

Abstract

In this paper, a fuzzy clustering approach for TS fuzzy model identification is presented. In the proposed method, the modified mountainx clustering algorithm is employed to determine the number of clusters. Secondly, the fuzzy c-regression model (FCRM) algorithm is used to obtain an optimal fuzzy partition matrix. As a result, the initial parameters can be determined by the optimal fuzzy partition. Finally, gradient descent algorithm is adopted to precisely adjust premise parameters and consequent parameters simultaneously. The simulation results reveal that the proposed algorithm can model an unknown system with a small number of fuzzy rules.

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Acknowledgments

The work was supported by Natural Science Foundation of Fujian Province of China (No.2011J01013), and Special Fund of Science, Technology of Fujian Provincial University of China (JK2010013) and Fund of Science, Technology of Xiamen (No. 3502Z20123022), The Projects of Education Department of Fujian Province (JK2010031, JA10196).

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou , 350108, China

    Mei-jiao Lin

  2. School of Science, Jimei University, Xiamen , 361021, China

    Shui-li Chen

Authors
  1. Mei-jiao Lin
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  2. Shui-li Chen
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Correspondence to Mei-jiao Lin .

Editor information

Editors and Affiliations

  1. School of Mathematics and Information Science, Guangzhou University Higher Education Mega Center, Guangzhou, People's Republic of China

    Bing-Yuan Cao

  2. Department of Mathematics and Computer Sciences, Mazandaran University, Babolsar, Iran

    Hadi Nasseri

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Lin, Mj., Chen, Sl. (2014). A Fuzzy Clustering Approach for TS Fuzzy Model Identification. In: Cao, BY., Nasseri, H. (eds) Fuzzy Information & Engineering and Operations Research & Management. Advances in Intelligent Systems and Computing, vol 211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38667-1_29

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  • DOI: https://doi.org/10.1007/978-3-642-38667-1_29

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38666-4

  • Online ISBN: 978-3-642-38667-1

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