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Recursive Identification of MIMO Wiener Systems

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2 Author(s)
Bi-Qiang Mu ; Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, P. R. China ; Han-Fu Chen

Stochastic approximation (SA) algorithms are proposed to identify a multi-input and multi-output (MIMO) Wiener system, in which the system input is taken to be a sequence of independent and identically distributed (i.i.d.) Gaussian random vectors ukN(0,I). The algorithm for identifying the nonlinear part is designed with multi-variable kernel functions. Under suitable conditions, we show that the estimates of the coefficients of the linear subsystem and of the values of the nonlinear function converge to the respective true values with probability one.

Published in:

IEEE Transactions on Automatic Control  (Volume:58 ,  Issue: 3 )