Learning in a time-varying environment by making use of the stochastic approximation and orthogonal series-type kernel probabilistic neural network

Authors:
Jacek M. Zurada;Maciej Jaworski
Affiliations:
Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY;Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland
Venue:
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
Year:
2011

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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.