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A Comparative Study of Type-2 Fuzzy System Optimization Based on Parameter Uncertainty of Membership Functions

  • Patricia Melin
Part of the Studies in Computational Intelligence book series (SCI, volume 389)

Abstract

A comparative study of type-2 fuzzy inference systems optimization as an integration method of Modular Neural Networks (MNNs) is presented [32]. 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 [34, 35]. 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

Membership Function Fuzzy System Fuzzy Inference System Soft Computing Benchmark Problem 
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 2012

Authors and Affiliations

  • Patricia Melin

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