Fuzzy Modeling with Fuzzy Adaptive Simulated Annealing

  • Hime Aguiar e Oliveira Junior
  • Lester Ingber
  • Antonio Petraglia
  • Mariane Rembold Petraglia
  • Maria Augusta Soares Machado
Part of the Intelligent Systems Reference Library book series (ISRL, volume 35)

Abstract

Data-based fuzzy system modeling usually depends on effective optimization methods to fit experimental data to parametric fuzzy models. Here, an approach that uses Takagi-Sugeno models and Adaptive Simulated Annealing (ASA) is presented and discussed, showing that (Fuzzy) ASA could also be helpful in such a kind of task. The problem to solve is well-defined - given a training set containing a finite number of input-output pairs, construct a fuzzy system approximating the behavior of the actual system that originated that set, within a pre-established precision. Such an approximation must have generalization ability to be useful in the real world, considering the finiteness of the training set and other constraints. Besides, other suggestions for application of (Fuzzy) ASA to fuzzy logic related problems are offered.

Keywords

Fuzzy System Cluster Center Fuzzy Modeling Fuzzy Cluster Very High 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Hime Aguiar e Oliveira Junior
    • 1
  • Lester Ingber
    • 2
  • Antonio Petraglia
    • 3
  • Mariane Rembold Petraglia
    • 3
  • Maria Augusta Soares Machado
    • 4
  1. 1.Rio de JaneiroBrazil
  2. 2.Lester Ingber Research AshlandUSA
  3. 3.Faculdades IBMEC Rio de JaneiroBrazil
  4. 4.IBMEC-RJ Rio de JaneiroBrazil

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