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Identification of Hammerstein-Wiener Models
University of Newcastle, Australia .
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 .
2013 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, no 1, 70-81 p.Article in journal (Refereed) Published
Abstract [en]

This paper develops and illustrates a new maximum-likelihood based method for the identification of Hammerstein-Wiener model structures. A central aspect is that a very general situation is considered wherein multivariable data, non-invertible Hammerstein and Wiener nonlinearities, and colored stochastic disturbances both before and after the Wiener nonlinearity are all catered for. The method developed here addresses the blind Wiener estimation problem as a special case.

Place, publisher, year, edition, pages
Elsevier, 2013. Vol. 49, no 1, 70-81 p.
Keyword [en]
System identification, Hammerstein, Wiener, Block-oriented models, Nonlinear models, Dynamic systems, Monte Carlo method, Smoothing, Expectation maximization algorithm, Particle methods, Maximum Likelihood
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-89528DOI: 10.1016/j.automatica.2012.09.018ISI: 000313772600008OAI: oai:DiVA.org:liu-89528DiVA: diva2:608221
Funder
Swedish Research Council
Note

Funding Agencies|Australian Research Council||project Calibrating Nonlinear Dynamical Models|621-2010-5876|CADICS, a Linneaus Center||Swedish Research Council||

Available from: 2013-02-26 Created: 2013-02-26 Last updated: 2017-12-06

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Schön, ThomasLjung, Lennart

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  • apa
  • harvard1
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