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Efficient Parametric Adjustment of Fuzzy Inference System Using Unconstrained Optimization

  • Ivan Nunes da Silva
  • Rogerio Andrade Flauzino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)

Abstract

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology.

Keywords

Fuzzy systems tuning algorithm system optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ivan Nunes da Silva
    • 1
  • Rogerio Andrade Flauzino
    • 2
  1. 1.University of São Paulo, Department of Electrical Engineering, CP 359, CEP 13566.590, São Carlos, SPBrazil
  2. 2.São Paulo State University, Department of Production Engineering, CP 473, CEP 17033.360, Bauru, SPBrazil

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