Learning in a Time-Varying Environment by Making Use of the Stochastic Approximation and Orthogonal Series-Type Kernel Probabilistic Neural Network

  • Jacek M. Zurada
  • Maciej Jaworski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7203)

Abstract

In the paper stochastic approximation, in combining with general regression neural network, is applied for learning in a time-varying environment. The orthogonal-type kernel is applied to design the general regression neural networks. Sufficient conditions for weak convergence are given and simulation results are presented.

Keywords

IEEE Transaction Probabilistic Neural Network General Regression Neural Network Orthogonal Series Multiple Fourier Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jacek M. Zurada
    • 1
  • Maciej Jaworski
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of LouisvilleLouisvilleUSA
  2. 2.Department of Computer EngineeringCzestochowa University of TechnologyCzestochowaPoland

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