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T-S Fuzzy Model Identification Based on Chaos Optimization

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Advances in Neural Networks - ISNN 2008 (ISNN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5263))

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Abstract

Nonlinear system identification by fuzzy model has been widely applied in many fields, for fuzzy model has the ability to approximate any nonlinear system to a given accuracy. In this paper, a nonlinear system identification algorithm based on Takagi-Sugeno (T-S) fuzzy model is proposed. Considering that the fuzzy space structure of T-S fuzzy model has great influence upon precision and effect of the final identification result, a new fuzzy clustering method based on chaos optimization, which is believed to be more accurate in data clustering, is adopted to partition the fuzzy space and obtain the fuzzy rules. Based on the fuzzy space partition and the subsequent structure parameters, the least square method is used to identify the conclusion parameters. Typical nonlinear function has been simulated to test the precision and effect of the proposed identification technique, and finally the algorithm has been successfully applied in the boiler-turbine system identification.

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Authors and Affiliations

  1. College of Hydroelectric Digitization Engineering, Huazhong University of Science and Technology, 430074, Wuhan, China

    Chaoshun Li, Jianzhong Zhou, Xueli An, Yaoyao He & Hui He

Authors
  1. Chaoshun Li
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  2. Jianzhong Zhou
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  3. Xueli An
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  4. Yaoyao He
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  5. Hui He
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghu University, 100084, Beijing, China

    Fuchun Sun

  2. Institute TAMS (Technical Aspects of Multimodal Systems), department of Informatics, University of Hamburg, Vogt-Koelln-Straße 30, 22527, Hamburg, Germany

    Jianwei Zhang

  3. Intel China Research Center, 8/F, Peking University, Department of Machine Intelligence, 100871, Beijing, China

    Ying Tan

  4. Department of Mathematics, Southeast University, 210096, Nanjing, China

    Jinde Cao

  5. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

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© 2008 Springer-Verlag Berlin Heidelberg

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Li, C., Zhou, J., An, X., He, Y., He, H. (2008). T-S Fuzzy Model Identification Based on Chaos Optimization. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_87

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  • DOI: https://doi.org/10.1007/978-3-540-87732-5_87

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87731-8

  • Online ISBN: 978-3-540-87732-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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