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Fast evolutionary learning of minimal radial basis function neural networks using a genetic algorithm

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Evolutionary Computing (AISB EC 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1143))

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Abstract

A hybrid algorithm for determining Radial Basis Function (RBF) networks is proposed. Evolutionary learning is applied to the non-linear problem of determining RBF network architecture (number of hidden layer nodes, basis function centres and widths) in conjunction with supervised gradient-based learning for tuning connection weights. A direct encoding of RBF hidden layer node basis function centres and widths is employed. The genetic operators utilised are adapted from those used in recent work on evolution of fuzzy inference systems. A parsimonious allocation of training sets and training epochs to evaluation of candidate networks during evolution is proposed in order to accelerate the learning process.

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Authors and Affiliations

  1. Faculty of Engineering, University of the West of England, Coldharbour Lane, BS16 1QY, Bristol

    Brian Carse

  2. Department of Computer Studies, Napier University, Craiglockhart Campus, 219 Collinton Rd, EH14 1DG, Edinburgh

    Terence C. Fogarty

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  1. Brian Carse
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  2. Terence C. Fogarty
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Terence C. Fogarty

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© 1996 Springer-Verlag Berlin Heidelberg

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Carse, B., Fogarty, T.C. (1996). Fast evolutionary learning of minimal radial basis function neural networks using a genetic algorithm. In: Fogarty, T.C. (eds) Evolutionary Computing. AISB EC 1996. Lecture Notes in Computer Science, vol 1143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032769

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  • DOI: https://doi.org/10.1007/BFb0032769

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61749-5

  • Online ISBN: 978-3-540-70671-7

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