Title :
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
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7282-4
DOI :
10.1109/CEC.2002.1004538
