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Strong Convergence of the Recursive Parzen-Type Probabilistic Neural Network Handling Nonstationary Noise

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Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7267))

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Abstract

A recursive version of the Parzen-type general regression neural network is studied. Strong convergence is established assuming time-varying noise. Experimental results are discussed in details.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Czestochowa University of Technology, ul. Armii Krajowej 36, 42-200, Czestochowa, Poland

    Lena Pietruczuk

  2. Department of Computer Science, Meiji University, Tama-ku, Kawasaki, 214-8571, Japan

    Yoichi Hayashi

Authors
  1. Lena Pietruczuk
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  2. Yoichi Hayashi
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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, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

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

    Lotfi A. Zadeh

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

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Pietruczuk, L., Hayashi, Y. (2012). Strong Convergence of the Recursive Parzen-Type Probabilistic Neural Network Handling Nonstationary Noise. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_19

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  • DOI: https://doi.org/10.1007/978-3-642-29347-4_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29346-7

  • Online ISBN: 978-3-642-29347-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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