A review of genetic algorithms applied to training radial basis function networks

被引:5
|
作者
Harpham, C [1 ]
Dawson, CW
Brown, MR
机构
[1] Kings Coll London, Dept Geog, London WC2R 2LS, England
[2] Loughborough Univ Technol, Dept Comp Sci, Loughborough LE11 3TU, Leics, England
[3] Univ Cent Lancashire, Dept Comp, Preston PR1 2HE, Lancs, England
来源
NEURAL COMPUTING & APPLICATIONS | 2004年 / 13卷 / 03期
关键词
artificial neural network; genetic algorithm; multilayer perceptron; radial basis function;
D O I
10.1007/s00521-004-0404-5
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The problems associated with training feed-forward artificial neural networks (ANNs) such as the multilayer perceptron (MLP) network and radial basis function (RBF) network have been well documented. The solutions to these problems have inspired a considerable amount of research, one particular area being the application of evolutionary search algorithms such as the genetic algorithm (GA). To date, the vast majority of GA solutions have been aimed at the MLP network. This paper begins with a brief overview of feedforward ANNs and GAs followed by a review of the current state of research in applying evolutionary techniques to training RBF networks.
引用
收藏
页码:193 / 201
页数:9
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