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Recursive Identification of Wiener-Hammerstein Systems with Nonparametric Nonlinearity

Published online by Cambridge University Press:  28 May 2015

Xiao-Li Hu  and
Yue-Ping Jiang
Xiao-Li Hu*
Affiliation:
School of Electrical Engineering and Computer Science, University of Newcastle, Newcastle NSW 2308, Australia
Yue-Ping Jiang
Affiliation:
School of Natural Sciences, Nanjing University of Posts and Telecommunications, Nanjing 210023, Jiangsu Province, P. R. China
*
Corresponding author. Email Address: [email protected]
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Abstract

A recursive scheme is proposed for identifying a single input single output (SISO) Wiener-Hammerstein system, which consists of two linear dynamic subsystems and a sandwiched nonparametric static nonlinearity. The first linear block is assumed to be a finite impulse response (FIR) filter and the second an infinite impulse response (IIR) filter. By letting the input be a sequence of mutually independent Gaussian random variables, the recursive estimates for coefficients of the two linear blocks and the value of the static nonlinear function at any fixed given point are proven to converge to the true values, with probability one as the data size tends to infinity. The static nonlinearity is identified in a nonparametric way and no structural information is directly used. A numerical example is presented that illustrates the theoretical results.

Keywords

60G3562F1293E12Wiener-Hammerstein systemnonparametric nonlinearityrecursive estimatestrong consistence
Type
Research Article
Information
East Asian Journal on Applied Mathematics , Volume 3 , Issue 4 , November 2013 , pp. 311 - 332
Copyright
Copyright © Global-Science Press 2013

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