An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms

This paper proposes an optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty (FOU) of the membership functions, considering three different cases to reduce the complexity problem of searching the parameter space of solutions. For the optimization meth...

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Bibliographic Details
Published in:Expert systems with applications 2012-03, Vol.39 (4), p.4590-4598
Main Authors: Hidalgo, Denisse, Melin, Patricia, Castillo, Oscar
Format: Article
Language:English
Subjects:
Design of fuzzy systems
Footprint of uncertainty
Footprints
Fuzzy
Fuzzy logic
Fuzzy set theory
Genetic algorithms
Inference
Optimization
Type-2 fuzzy logic
Uncertainty
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Summary:This paper proposes an optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty (FOU) of the membership functions, considering three different cases to reduce the complexity problem of searching the parameter space of solutions. For the optimization method, we propose the use of a genetic algorithm (GA) to optimize the type-2 fuzzy inference systems, considering different cases for changing the level of uncertainty of the membership functions to reach the optimal solution at the end.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2011.10.003