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Nature Inspired Methods in the Radial Basis Function Network Learning Process

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Artificial Neural Networks - ICANN 2008 (ICANN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5164))

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Abstract

In present we benefit from the use of nature processes which provide us with highly effective heuristics for solving various problems. Their advantages are mainly prominent in hybrid approach. This paper evaluates several approaches for learning neural network based on Radial Basis Function (RBF) for distinguishing different sets in \(\mathcal{R}^{L}\). RBF networks use one layer of hidden RBF units and the number of RBF units is kept constatnt. In the paper we evaluate the ACO\(_\mathcal{R}\) (Ant Colony Approach for Real domain) approach inspired by ant behavior and the PSO (Particle Swarm Optimization) algorithm inspired by behavior of flock of birds or fish in the nature. Nature inspired and classical algorithms are compared and evaluated.

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Authors and Affiliations

  1. Gerstner Laboratory, BioDat Research Group, Czech Technical University in Prague,  

    Miroslav Burša & Lenka Lhotská

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  1. Miroslav Burša
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  2. Lenka Lhotská
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Véra Kůrková Roman Neruda Jan Koutník

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Burša, M., Lhotská, L. (2008). Nature Inspired Methods in the Radial Basis Function Network Learning Process. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_86

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  • DOI: https://doi.org/10.1007/978-3-540-87559-8_86

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87558-1

  • Online ISBN: 978-3-540-87559-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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