On the application of the Parzen-type kernel probabilistic neural network and recursive least squares method for learning in a time-varying environment

MacIej Jaworski; Yoichi Hayashi

Conference Proceedings

On the application of the Parzen-type kernel probabilistic neural network and recursive least squares method for learning in a time-varying environment

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2012) 7203 LNCS(PART 1) 490-500

DOI: 10.1007/978-3-642-31464-3_50

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Abstract

This paper presents the Parzen kernel-type regression neural network in combination with recursive least squares method to solve problem of learning in a time-varying environment. Sufficient conditions for convergence in probability are given. Simulation experiments are presented and discussed. © 2012 Springer-Verlag.

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Jaworski, M., & Hayashi, Y. (2012). On the application of the Parzen-type kernel probabilistic neural network and recursive least squares method for learning in a time-varying environment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7203 LNCS, pp. 490–500). https://doi.org/10.1007/978-3-642-31464-3_50

On the application of the Parzen-type kernel probabilistic neural network and recursive least squares method for learning in a time-varying environment

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