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A Novel Identification Method for Generalized T-S Fuzzy Systems

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  • Ling Huang
  • Kai Wang
  • Peng Shi
  • Hamid Reza Karimi

Abstract

In order to approximate any nonlinear system, not just affine nonlinear systems, generalized T-S fuzzy systems, where the control variables and the state variables, are all premise variables are introduced in the paper. Firstly, fuzzy spaces and rules were determined by using ant colony algorithm. Secondly, the state-space model parameters are identified by using genetic algorithm. The simulation results show the effectiveness of the proposed algorithm.

Suggested Citation

  • Ling Huang & Kai Wang & Peng Shi & Hamid Reza Karimi, 2012. "A Novel Identification Method for Generalized T-S Fuzzy Systems," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-12, November.
    • Handle: RePEc:hin:jnlmpe:893807
    DOI: 10.1155/2012/893807
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