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Radial Basis Function Neural Networks: Theory and Applications

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Neural Networks and Soft Computing

Part of the book series: Advances in Soft Computing ((AINSC,volume 19))

Abstract

Essential theory and main applications of feed-forward connectionist structures termed radial basis function (RBF) neural networks are given. Universal approximation and Cover’s theorems are outlined that justify powerful RBF network capabilities in function approximation and data classification tasks. The methods for regularising RBF generated mappings are addressed also. Links of these networks to kernel regression methods, density estimation, and nonlinear principal component analysis are pointed out. Particular attention is put on discussing different RBF network training schemes, e.g. the constructive method incorporating orthogonalisation of RBF kernels. Numerous, successful RBF networks applications in diverse fields such as signal modelling, non-linear time series prediction, identification of dynamic systems, pattern recognition, and knowledge discovery are outlined.

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Author information

Authors and Affiliations

  1. Institute of Electronics, Technical University of Łódź, 18/22 Stefanowskiego, 90-924, Łódź, Poland

    Paweł Strumiłło

  2. Faculty of Process and Environmental Engineering, Technical University of Łódź, 213 Wólczańska, 93-005, Łódź, Poland

    Władysław Kamiński

Authors
  1. Paweł Strumiłło
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  2. Władysław Kamiński
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Technical University Częstochowa, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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© 2003 Springer-Verlag Berlin Heidelberg

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Strumiłło, P., Kamiński, W. (2003). Radial Basis Function Neural Networks: Theory and Applications. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_14

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  • DOI: https://doi.org/10.1007/978-3-7908-1902-1_14

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-0005-0

  • Online ISBN: 978-3-7908-1902-1

  • eBook Packages: Springer Book Archive

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