Single Minimum Nonlinearity Wiener System Identification by Weighted Principal Component Analysis

Abstract : Wiener system identification with a finite impulse response (FIR) model is investigated in this paper, focusing on the challenging case of non Gaussian input distribution and non monotonic nonlinearity. The proposed method assumes that the static nonlinear function of the Wiener system has a single minimum (or maximum), but does not assume any parametrization of the nonlinear function. Based on a modified principal component analysis (PCA), referred to as weighted PCA, the FIR coefficients of the Wiener system are estimated without estimating the nonlinear function of the Wiener system. The numerical computation cost is essentially equivalent to those of two standard PCA. Numerical examples in harsh practical conditions, with data generated by Wiener systems involving a discontinuous nonlinear function or an infinite impulse response, are presented to illustrate the robustness and effectiveness of the proposed method.
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Communication dans un congrès
Giri, Fouad and Van Assche, Vincent. 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, Jul 2013, Caen, France. IFAC, 11 - Part 1, pp.384-389, 2013, 〈10.3182/20130703-3-FR-4038.00061〉
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https://hal.inria.fr/hal-00854679
Contributeur : Qinghua Zhang <>
Soumis le : mardi 27 août 2013 - 18:20:32
Dernière modification le : mercredi 29 novembre 2017 - 15:11:08

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Qinghua Zhang, Vincent Laurain. Single Minimum Nonlinearity Wiener System Identification by Weighted Principal Component Analysis. Giri, Fouad and Van Assche, Vincent. 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, Jul 2013, Caen, France. IFAC, 11 - Part 1, pp.384-389, 2013, 〈10.3182/20130703-3-FR-4038.00061〉. 〈hal-00854679〉

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