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Robustness of Radial Basis Functions

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Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

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Abstract

Neural networks are intended to be used in future nanoelectronics since these architectures seem to be robust against malfunctioning elements and noise. In this paper we analyze the robustness of radial basis function networks and determine upper bounds on the mean square error under noise contaminated weights and inputs.

This work was supported by the Graduate College 776 – Automatic Configuration in Open Systems- funded by the Deutsche Forschungsgemeinschaft.

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Author information

Authors and Affiliations

  1. Heinz Nixdorf Institute, System and Circuit Technology, University of Paderborn, Germany

    Ralf Eickhoff & Ulrich Rückert

Authors
  1. Ralf Eickhoff
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  2. Ulrich Rückert
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Editor information

Editors and Affiliations

  1. Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, E08034, Barcelona, Spain

    Joan Cabestany

  2. Department of Computer Architecture and Computer Technology, University of Granada,  

    Alberto Prieto

  3. Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Francisco Sandoval

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© 2005 Springer-Verlag Berlin Heidelberg

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Eickhoff, R., Rückert, U. (2005). Robustness of Radial Basis Functions. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_33

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  • DOI: https://doi.org/10.1007/11494669_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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