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)


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.


Membership Function Fuzzy System Fuzzy Inference System Soft Computing Benchmark Problem 
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© Springer-Verlag Berlin Heidelberg 2012

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  • Patricia Melin

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