Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14247
Title: Learning of neural network parameters using a fuzzy genetic algorithm
Authors: Ling, SH
Lam, HK
Leung, FHF 
Tam, PKS
Keywords: Fuzzy logic
Genetic algorithms
Learning (artificial intelligence)
Neural nets
Search problems
Issue Date: 2002
Publisher: IEEE
Source: Proceedings of the 2002 Congress on Evolutionary Computation, 2002 : CEC '02, May 2002, Honolulu, HI, p. 1928-1933 How to cite?
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
URI: http://hdl.handle.net/10397/14247
ISBN: 0-7803-7282-4
DOI: 10.1109/CEC.2002.1004538
Appears in Collections:Conference Paper

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