Recursive identification for Wiener systems using Gaussian inputs

The recursive algorithms are given for identifying the single-input single-output Wiener system which consists of a moving average type linear subsystem followed by a static nonparametric nonlinearity. The input is defined to be a sequence of mutually independent Gaussian random variables. The estimates for coefficients of the linear subsystem as well as for f(v) at any v are proved to converge to the true values with probability one. A numerical example is given, justifying the theoretical analysis. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

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