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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2006: Artificial Intelligence and Soft Computing – ICAISC 2006 pp 46–55Cite as

Nonlinear Function Learning by the Normalized Radial Basis Function Networks

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4029))

Abstract

We study strong universal consistency and the rates of convergence of nonlinear regression function learning algorithms using normalized radial basis function networks. The parameters of the network including centers, covariance matrices and synaptic weights are trained by the empirical risk minimization. We show the rates of convergence for the networks whose parameters are learned by the complexity regularization.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada, H3G 1M8

    Adam Krzyżak

  2. Institute of Control Engineering, Technical University of Szczecin, 70-313, Szczecin, Poland

    Adam Krzyżak

  3. Dominik Schäfer, Fachbereich Mathematik, Universität Stuttgart, D-70569, Stuttgart, Germany

    Dominik Schäfer

Authors
  1. Adam Krzyżak
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  2. Dominik Schäfer
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Berkeley Initiative in Soft Computing (BISC), University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Żurada

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© 2006 Springer-Verlag Berlin Heidelberg

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Krzyżak, A., Schäfer, D. (2006). Nonlinear Function Learning by the Normalized Radial Basis Function Networks. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_6

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  • DOI: https://doi.org/10.1007/11785231_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35748-3

  • Online ISBN: 978-3-540-35750-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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