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)


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.


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


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    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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