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Nonlinear Regression with Piecewise Affine Models Based on RBFN

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Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3697))

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Abstract

In this paper, a modeling method of high dimensional piecewise affine models is proposed. Because the model interpolates the outputs at the orthogonal grid points in the input space, the shape of the piecewise affine model is easily understood. The interpolation is realized by a RBFN, whose function is defined with max-min functions. By increasing the number of RBFs, the capability to express nonlinearity can be improved. In this paper, an algorithm to determine the number and locations of RBFs is proposed.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .

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Author information

Authors and Affiliations

  1. Department of Systems Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, 466-8555, Japan

    Masaru Sakamoto, Dong Duo, Yoshihiro Hashimoto & Toshiaki Itoh

Authors
  1. Masaru Sakamoto
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  2. Dong Duo
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  3. Yoshihiro Hashimoto
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  4. Toshiaki Itoh
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Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

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

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

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

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Sakamoto, M., Duo, D., Hashimoto, Y., Itoh, T. (2005). Nonlinear Regression with Piecewise Affine Models Based on RBFN. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_14

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  • DOI: https://doi.org/10.1007/11550907_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28755-1

  • Online ISBN: 978-3-540-28756-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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