A Comparative Study of Type-2 Fuzzy System Optimization Based on Parameter Uncertainty of Membership Functions
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 [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.
KeywordsMembership Function Fuzzy System Fuzzy Inference System Soft Computing Benchmark Problem
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