Learning in a time-varying environment by making use of the stochastic approximation and orthogonal series-type kernel probabilistic neural network

Jacek M. Zurada; MacIej Jaworski

Conference Proceedings

Learning in a time-varying environment by making use of the stochastic approximation and orthogonal series-type kernel probabilistic neural network

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2012) 7203 LNCS(PART 1) 539-548

DOI: 10.1007/978-3-642-31464-3_55

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Abstract

In the paper stochastic approximation, in combining with general regression neural network, is applied for learning in a time-varying environment. The orthogonal-type kernel is applied to design the general regression neural networks. Sufficient conditions for weak convergence are given and simulation results are presented. © 2012 Springer-Verlag.

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Zurada, J. M., & Jaworski, M. (2012). Learning in a time-varying environment by making use of the stochastic approximation and orthogonal series-type kernel probabilistic neural network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7203 LNCS, pp. 539–548). https://doi.org/10.1007/978-3-642-31464-3_55

Learning in a time-varying environment by making use of the stochastic approximation and orthogonal series-type kernel probabilistic neural network

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