Type-2 fuzzy inference system optimization based on the uncertainty of membership functions applied to benchmark problems

Authors:
Denisse Hidalgo;Patricia Melin;Oscar Castillo
Affiliations:
School of Engineering, UABC University, Tijuana, México;Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, México;Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, México
Venue:
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Year:
2010

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Abstract

In this paper we describe a method for the optimization of type-2 fuzzy systems based on the level of uncertainty considering three different cases to reduce the complexity problem of searching the solution space. The proposed method produces the best fuzzy inference systems for particular applications based on a genetic algorithm. We apply a Genetic Algorithm to find the optimal type-2 fuzzy system dividing the search space in three subspaces. We show the comparative results obtained for the benchmark problems.