Maximum likelihood identification of Wiener models

Title: Maximum likelihood identification of Wiener models
Creator: Hagenblad, Anna; Ljung, Lennart; Wills, Adrian
Relation: Automatica Vol. 44, Issue 11, p. 2697-2705
Publisher Link: http://dx.doi.org/10.1016/j.automatica.2008.02.016
Publisher: Elsevier
Resource Type: journal article
Date: 2008
Description: The Wiener model is a block oriented model, having a linear dynamic system followed by a static nonlinearity. The dominating approach to estimate the components of this model has been to minimize the error between the simulated and the measured outputs. We show that this will, in general, lead to biased estimates if there are other disturbances present than measurement noise. The implications of Bussgang’s theorem in this context are also discussed. For the case with general disturbances, we derive the Maximum Likelihood method and show how it can be efficiently implemented. Comparisons between this new algorithm and the traditional approach, confirm that the new method is unbiased and also has superior accuracy.
Subject: system identification; nonlinearities; Wiener model; maximum likelihood; prediction error method
Identifier: uon:5588
Identifier: http://hdl.handle.net/1959.13/43489
Identifier: ISSN:0005-1098
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