DocumentCode
2146923
Title
Optimal Design of Type-2 Fuzzy Membership Functions Using Genetic Algorithms in a Partitioned Search Space
Author
Hidalgo, Denisse ; Melin, Patricia ; Castillo, Oscar
Author_Institution
UABC Univ., Tijuana, Mexico
fYear
2010
fDate
14-16 Aug. 2010
Firstpage
212
Lastpage
216
Abstract
In this paper we describe an evolutionary method for the optimization of type-2 fuzzy systems based on the level of uncertainty. The proposed evolutionary method produces the best fuzzy inference systems (based on the memberships functions) for particular applications. The optimization of membership functions of the type-2 fuzzy systems is based on the level of uncertainty considering three different cases to reduce the complexity problem of searching the solution space.
Keywords
fuzzy set theory; fuzzy systems; genetic algorithms; search problems; evolutionary method; fuzzy inference systems; genetic algorithms; partitioned search space; type-2 fuzzy membership functions; type-2 fuzzy system optimisation; Artificial neural networks; Benchmark testing; Fuzzy logic; Fuzzy systems; Optimization; Simulation; Uncertainty; Genetic Algrithm; Modular Neural Networks; Type-2 Fuzzy Logic;
fLanguage
English
Publisher
ieee
Conference_Titel
Granular Computing (GrC), 2010 IEEE International Conference on
Conference_Location
San Jose, CA
Print_ISBN
978-1-4244-7964-1
Type
conf
DOI
10.1109/GrC.2010.57
Filename
5576130
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