2012 | OriginalPaper | Chapter
Learning in a Time-Varying Environment by Making Use of the Stochastic Approximation and Orthogonal Series-Type Kernel Probabilistic Neural Network
Authors : Jacek M. Zurada, Maciej Jaworski
Published in: Parallel Processing and Applied Mathematics
Publisher: Springer Berlin Heidelberg
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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.