On the strong convergence of the recursive orthogonal series-type kernel probabilistic neural networks handling time-varying noise

Authors:
Piotr Duda;Marcin Korytkowski
Affiliations:
Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland;Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland
Venue:
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Year:
2012

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Abstract

Sufficient conditions for strong convergence of recursive general regression neural networks are given assuming nonstationary noise. The orthogonal series-type kernel is applied. Simulation results show convergence even if variance of noise diverges to infinity.