Strong convergence of the parzen-type probabilistic neural network in a time-varying environment

Authors:
Lena Pietruczuk;Meng Joo Er
Affiliations:
Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland;School of Electrical and Electonic 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 this paper general regression neural networks are applied to handle nonstationary noise. Strong convergence is established. Experiments conducted on synthetic data show good performance in the case of finite length of data samples.