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Maximum Likelihood Identification of Wiener Models with a Linear Regression Initialization
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.
1998 (English)In: Proceedings of the 37th IEEE Conference on Decision and Control, 1998, 712-713 vol.1 p.Conference paper, Published paper (Refereed)
Abstract [en]

Many parametric identification routines suffer from the problem with local minima. This is true also for the prediction-error approach to identifying Wiener models, i.e. linear models with a static non-linearity at the output. We here suggest a linear regression initialization, that secures a consistent and efficient estimate, when used in conjunction with a Gauss-Newton minimization scheme.

Place, publisher, year, edition, pages
1998. 712-713 vol.1 p.
Keyword [en]
Nonlinear identification, Wiener models, Linear systems, Maximum likelihood estimation, Recursive estimation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-91596DOI: 10.1109/CDC.1998.760768ISBN: 0-7803-4394-8 (print)OAI: oai:DiVA.org:liu-91596DiVA: diva2:625367
Conference
37th IEEE Conference on Decision and Control, Tampa, FL, USA, December, 1998
Available from: 2013-06-04 Created: 2013-04-28 Last updated: 2013-06-04

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Ljung, LennartHagenblad, Anna

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