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Recursive identification for Wiener systems using Gaussian inputs
Author(s) -
Hu XiaoLi,
Chen HanFu
Publication year - 2008
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.27
Subject(s) - gaussian , nonlinear system , sequence (biology) , nonparametric statistics , mathematics , identification (biology) , random variable , control theory (sociology) , algorithm , computer science , control (management) , statistics , artificial intelligence , genetics , physics , botany , quantum mechanics , biology
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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