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Random Sieve Based on Projections for RBF Neural Net Structure Selection

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Artificial Intelligence and Soft Computing (ICAISC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7894))

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Abstract

Our aim is to propose a method for selecting a radial basis functions terms to be included into a neural net model. As it is frequently met in practice, we consider the case of a deficit in the admissible number of observations (learning sequence) in comparison with a much larger number of candidate terms. The proposed approach is based on a random sieve that aims at selecting only necessary RBF’s by a hierarchy of a large number of random mixing of candidate RBF’s and testing their significance. The results of simulations are also reported.

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Author information

Authors and Affiliations

  1. Institute of Computer Engineering control and Robotics, Wrocław University of Technology, Wrocław, Poland

    Ewa Skubalska-Rafajłowicz & Ewaryst Rafajłowicz

Authors
  1. Ewa Skubalska-Rafajłowicz
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  2. Ewaryst Rafajłowicz
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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Skubalska-Rafajłowicz, E., Rafajłowicz, E. (2013). Random Sieve Based on Projections for RBF Neural Net Structure Selection. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_18

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  • DOI: https://doi.org/10.1007/978-3-642-38658-9_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38657-2

  • Online ISBN: 978-3-642-38658-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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