On the cesaro orthogonal series-type kernel probabilistic neural networks handling non-stationary noise

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

Citing 6
Cited 0

Sequential pattern recognition procedures derived from multiple Fourier series

Pattern Recognition Letters
Connectionist Structures of Type 2 Fuzzy Inference Systems

PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
On Bayes Risk Consistent Pattern Recognition Procedures in a Quasi-Stationary Environment

IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting ensemble of relational neuro-fuzzy systems

ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Multiple Fourier series procedures for extraction of nonlinearregressions from noisy data

IEEE Transactions on Signal Processing
Identification of MISO nonlinear regressions in the presence of a wide class of disturbances

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Cesaro means of orthogonal series are applied to construct general regression neural networks. Sufficient conditions for convergence in probability are given assuming nonstationary noise. An experiment with syntetic data is described.