Learning of neural network parameters using a fuzzy genetic algorithm

Author

Ling, S.H. ; Lam, H.K. ; Leung, F.H.F. ; Tam, P.K.S.

Author_Institution

Centre for Multimedia Signal Process., Hong Kong Polytech.Univ., Kowloon, China

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1928

Lastpage

1933

Abstract

This paper presents the learning of neural network parameters using a fuzzy genetic algorithm (GA). The proposed fuzzy GA is modified from the traditional GA with arithmetic crossover and non-uniform mutation. By introducing modified genetic operations, it will be shown that the performance of the proposed fuzzy GA are better than the traditional GA based on some benchmark test functions. Using the fuzzy GA, the parameters of the neural networks can be tuned. An application example on sunspot forecasting is given to show the merits of the proposed fuzzy GA

Keywords

fuzzy logic; genetic algorithms; learning (artificial intelligence); neural nets; search problems; arithmetic crossover; benchmark test functions; fuzzy GA; fuzzy genetic algorithm; learning; neural network parameters; nonuniform mutation; performance; sunspot forecasting; Arithmetic; Benchmark testing; Biological cells; Fuzzy logic; Fuzzy neural networks; Genetic algorithms; Genetic mutations; Humans; Neural networks; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7282-4

Type

conf

DOI

10.1109/CEC.2002.1004538

Filename

1004538