Learning optimal fuzzy rules using simulated annealing

Author

Dickerson, Julie A.

Author_Institution

Dept. of Electr. & Comput. Eng., Iowa State Univ., Ames, IA, USA

fYear

1997

fDate

21-24 Sep 1997

Firstpage

102

Lastpage

105

Abstract

Fuzzy systems can uniformly approximate continuous functions, but the number of rules increases geometrically with system dimension. Fast simulated annealing that uses alpha stable generating functions to search locally and tunnel through space can solve large optimization problems. Alpha values less than 1 can find the optimal fuzzy rules that approximate a function. The thick tails of these distributions help the annealing algorithm quickly search the solution space. This method can find the fuzzy rules for one and two input fuzzy systems

Keywords

fuzzy systems; learning systems; search problems; simulated annealing; alpha stable generating functions; annealing algorithm; continuous function approximation; distributions; fast simulated annealing; fuzzy systems; large optimization problem solving; local search; one-input fuzzy systems; optimal fuzzy rule learning; space tunnelling; two-input fuzzy systems; Additives; Computational modeling; Cooling; Fuzzy sets; Fuzzy systems; Gaussian distribution; Least squares approximation; Probability distribution; Simulated annealing; Solid modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 1997. NAFIPS '97., 1997 Annual Meeting of the North American

Conference_Location

Syracuse, NY

Print_ISBN

0-7803-4078-7

Type

conf

DOI

10.1109/NAFIPS.1997.624019

Filename

624019