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System Identification of Nonlinear State-Space Models
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
University of Newcastle, Australia.
University of Newcastle, Australia.
2011 (English)In: Automatica, ISSN 0005-1098, Vol. 47, no 1, 39-49 p.Article in journal (Refereed) Published
Abstract [en]

This paper is concerned with the parameter estimation of a general class of nonlinear dynamic systems in state-space form. More specifically, a Maximum Likelihood (ML) framework is employed and an Expectation Maximisation (EM) algorithm is derived to compute these ML estimates. The Expectation (E) step involves solving a nonlinear state estimation problem, where the smoothed estimates of the states are required. This problem lends itself perfectly to the particle smoother, which provides arbitrarily good estimates. The maximisation (M) step is solved using standard techniques from numerical optimisation theory. Simulation examples demonstrate the efficacy of our proposed solution.

Place, publisher, year, edition, pages
Elsevier, 2011. Vol. 47, no 1, 39-49 p.
Keyword [en]
System identification, Nonlinear models, Dynamic systems, Monte Carlo method, Smoothing filters, Expectation maximisation algorithm, Particle methods
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-65958DOI: 10.1016/j.automatica.2010.10.013ISI: 000286704500004OAI: diva2:400661
Available from: 2011-02-28 Created: 2011-02-28 Last updated: 2013-09-23

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