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

被引：0
|
作者：
C. Harpham
C. W. Dawson
M. R. Brown
机构：
[1] King’s College London,Department of Geography
[2] Loughborough University,Department of Computer Science
[3] University of Central Lancashire,Department of Computing
来源：
Neural Computing & Applications | 2004年 / 13卷
关键词：
Artificial neural network; Genetic algorithm; Multilayer perceptron; Radial basis function;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The problems associated with training feedforward 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.
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页码：193 / 201
页数：8
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