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Maximum Likelihood Identification of Wiener Models
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
University of Newcastle, Australia.
2008 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 44, no 11, 2697-2705 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2008. Vol. 44, no 11, 2697-2705 p.
Keyword [en]
System identification, Nonlinearities, Wiener model, Maximum likelihood, Prediction error method
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-16239DOI: 10.1016/j.automatica.2008.02.016OAI: oai:DiVA.org:liu-16239DiVA: diva2:133498
Available from: 2009-01-12 Created: 2009-01-09 Last updated: 2017-12-14

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Hagenblad, AnnaLjung, Lennart

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