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The Takagi-Sugeno Fuzzy Model Identification Method of Parameter Varying Systems

  • Xie Keming
  • T. Y. Lin
  • Zhang Jianwei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1424)

Abstract

This paper presents the TS model identification method by which a great number of systems whose parameters vary dramatically with working states can be identified via Fuzzy Neural Networks (FNN). The suggested method could overcome the drawbacks of traditional linear system identification methods which are only effective under certain narrow working states and provide global dynamic description based on which further control of such systems may be carried out. Simulation results of a second-order parameter varying system demonstrate the effectiveness of the method.

Keywords

Parameter Varying Systems TS Fuzzy Model Fuzzy Neural Networks (FNN) Identification 

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References

  1. [1]
    B. Kosko, Neural Networks and Fuzzy Systems, Prentice Hall, 1992Google Scholar
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    T. Takagi and M. Sugeno, “Fuzzy identification of system and its applications to modeling and control”, IEEE Trans. on System Man and Cybernetics, vol. SMC-15, no. 1, 1985, pp. 116–132.Google Scholar
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    M. Sugeno and G. T. Kang, “Structure identification of fuzzy model”, Fuzzy Sets and Systems, vol. 28, 1988, pp. 15–33.zbMATHCrossRefMathSciNetGoogle Scholar
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    M. Sugeno and K. Tanaka, “Successive identification of a fuzzy model and its applications to prediction of a complex system”, Fuzzy Sets and Systems, vol. 42, 1992, pp. 315–334.CrossRefMathSciNetGoogle Scholar
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    Xie Keming and Zhang Jianwei. “A linear fuzzy model identification method based on fuzzy neural networks”, in Proceedings of the 2nd Worldwide Chinese Intelligence Control and Intelligence Automation Conference, 1997.Google Scholar
  6. [6]
    Xie Keming and Zhang Jianwei, “An Adaptive Backpropagation Algorithm Based on Error Rate of Change”, submitted to Journal of Taiyuan University of Technology.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Xie Keming
    • 1
  • T. Y. Lin
    • 2
  • Zhang Jianwei
    • 1
  1. 1.Department of AutomationTaiyuan University of TechnologyTaiyuanP. R. China
  2. 2.Department of Mathematics and Computer ScienceSan Jose State UniversitySan JoseUSA

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