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On the Application of the Parzen-Type Kernel Probabilistic Neural Network and Recursive Least Squares Method for Learning in a Time-Varying Environment

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Parallel Processing and Applied Mathematics (PPAM 2011)

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

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Abstract

This paper presents the Parzen kernel-type regression neural network in combination with recursive least squares method to solve problem of learning in a time-varying environment. Sufficient conditions for convergence in probability are given. Simulation experiments are presented and discussed.

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Author information

Authors and Affiliations

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

    Maciej Jaworski

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

    Yoichi Hayashi

Authors
  1. Maciej Jaworski
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  2. Yoichi Hayashi
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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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Jaworski, M., Hayashi, Y. (2012). On the Application of the Parzen-Type Kernel Probabilistic Neural Network and Recursive Least Squares Method for Learning in a Time-Varying Environment. 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_50

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  • DOI: https://doi.org/10.1007/978-3-642-31464-3_50

  • 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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