Learning in a non-stationary environment using the recursive least squares method and orthogonal-series type regression neural network

Authors:
Maciej Jaworski;Meng Joo Er
Affiliations:
Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
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 the recursive least squares method, in combining with general regression neural network, is applied for learning in a non-stationary environment. The orthogonal series-type kernel is applied to design the general regression neural networks. Sufficient conditions for convergence in probability are given and simulation results are presented.