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Neural Computing and Applications

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01.09.2004 | Original Article

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

verfasst von: C. Harpham, C. W. Dawson, M. R. Brown

Erschienen in: Neural Computing and Applications | Ausgabe 3/2004

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Abstract

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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Titel: A review of genetic algorithms applied to training radial basis function networks
verfasst von: C. Harpham
C. W. Dawson
M. R. Brown
Publikationsdatum: 01.09.2004
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 3/2004
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-004-0404-5

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