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On the Cesaro Orthogonal Series-Type Kernel Probabilistic Neural Networks Handling Non-stationary Noise

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Parallel Processing and Applied Mathematics (PPAM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7203))

Abstract

The Cesaro means of orthogonal series are applied to construct general regression neural networks. Sufficient conditions for convergence in probability are given assuming nonstationary noise. An experiment with syntetic data is described.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland

    Piotr Duda

  2. Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

Authors
  1. Piotr Duda
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  2. Jacek M. Zurada
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Editor information

Editors and Affiliations

  1. Institute of Computer and Information Science, Czestochowa University of Technology, Dabrowskiego 69, 42-201, Czestochowa, Poland

    Roman Wyrzykowski  & Konrad Karczewski  & 

  2. Electrical Engineering and Computer Science Department, University of Tennessee, 1122 Volunteer Blvd, 37996-3450, Knoxville, TN, USA

    Jack Dongarra

  3. Department of Informatics and Mathematical Modeling, Technical University of Denmark, Richard Petersens Plads, Building 321, 2800, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Duda, P., Zurada, J.M. (2012). On the Cesaro Orthogonal Series-Type Kernel Probabilistic Neural Networks Handling Non-stationary Noise. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2011. Lecture Notes in Computer Science, vol 7203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31464-3_44

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  • DOI: https://doi.org/10.1007/978-3-642-31464-3_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31463-6

  • Online ISBN: 978-3-642-31464-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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