GA-based learning of BMF fuzzy-neural network

Author

Wang, Wei-Yen ; Lee, Tsu-Tian ; Hsu, Chen-Chian ; Li, Yi-Hsum

Author_Institution

Dept. of Electron. Eng., Fu-Jen Catholic Univ., Taipei, Taiwan

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1234

Lastpage

1239

Abstract

An approach to adjust both control points of B-spline membership functions (BMFs) and weightings of fuzzy-neural networks using a simplified genetic algorithm (SGA) is proposed. The SGA is proposed by using a sequential-search-based crossover point (SSCP) method in which a better crossover point is determined and only the gene at the specified crossover point is crossed as a single point crossover operation. Chromosomes consisting of both the control points of BMFs and the weightings of fuzzy-neural networks are coded as an adjustable vector with real number components and searched by the SGA. Because of the use of the SGA, faster convergence of the evolution process to search for an optimal fuzzy-neural network can be achieved. Nonlinear functions approximated by using the fuzzy-neural networks via the SGA are demonstrated to illustrate the applicability of the proposed method

Keywords

function approximation; fuzzy neural nets; genetic algorithms; learning (artificial intelligence); nonlinear functions; splines (mathematics); B-spline membership function fuzzy-neural networks; BMF fuzzy-neural network; GA-based learning; control points; nonlinear functions; sequential-search-based crossover point method; simplified genetic algorithm; weightings; Control engineering; Convergence; Electronic mail; Function approximation; Fuzzy logic; Genetic algorithms; Genetic engineering; Learning systems; Neural networks; Spline;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7280-8

Type

conf

DOI

10.1109/FUZZ.2002.1006680

Filename

1006680