On the weak convergence of the recursive orthogonal series-type kernel probabilistic neural networks in a time-varying environment

Authors:
Piotr Duda;Yoichi Hayashi
Affiliations:
Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland;Department of Computer Science, Meiji University, Tama-ku, Kawasaki, Japan
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 a paper a recursive version of general regression neural networks, based on the orthogonal series-type kernels, is presented. Sufficient conditions for convergence in probability are given assuming time-varying noise. Experimental results are provided and discussed.