Comparative Study of Type-2 Fuzzy Inference System Optimization Based on the Uncertainty of Membership Functions

  • Denisse Hidalgo
  • Patricia Melin
  • Oscar Castillo
  • Guillermo Licea
Part of the Studies in Computational Intelligence book series (SCI, volume 312)

Abstract

A comparative study of type-2 fuzzy inference systems optimization as an integration method of Modular Neural Networks (MNNs) is presented. The optimization method for type-2 fuzzy systems is based on the footprint of uncertainty (FOU) of the membership functions. We use different benchmark problems to test the optimization method for the fuzzy systems. First, we tested the methodology by manually incrementing the percentage in the FOU, later we apply a Genetic Algorithm to find the optimal type-2 fuzzy system. We show the comparative results obtained for the benchmark problems.

Keywords

Genetic Algorithm Membership Function Fuzzy Logic Fuzzy System Fuzzy Inference System 
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 Berlin Heidelberg 2010

Authors and Affiliations

  • Denisse Hidalgo
    • 1
  • Patricia Melin
    • 2
  • Oscar Castillo
    • 2
  • Guillermo Licea
    • 1
  1. 1.UABC UniversityTijuanaMéxico
  2. 2.Tijuana Institute of TechnologyTijuanaMéxico

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