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Particle Filters for System Identification of State-Space Models Linear in either Parameters or States
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.
2004 (English)In: Proceedings of Reglermöte 2004, 2004, 1-6 p.Conference paper, Published paper (Other academic)
Abstract [en]

The potential use of the marginalized particle filter for nonlinear system identification is investigated. The particle filter itself offers a general tool for estimating unknown parameters in non-linear models of moderate complexity, and the basic trick is to model the parameters as a random walk (so called roughening noise) with decaying variance. We derive algorithms for systems which are non-linear in either the parameters or the states, but not both generally. In these cases, marginalization applies to the linear part, which firstly significantly widens the scope of the particle filter to more complex systems, and secondly decreases the variance in the linear parameters/states for fixed filter complexity. This second property is illustrated on an example of chaotic model. The particular case of freely parametrized linear state space models, common in subspace identification approaches, is bi-linear in states and parameters, and thus both cases above are satisfied. One can then choose which one to marginalize.

Place, publisher, year, edition, pages
2004. 1-6 p.
Keyword [en]
System identification, Nonlinear estimation, Recursive estimation, Particle filters, Kalman filters, Bayesian estimation
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-90351OAI: oai:DiVA.org:liu-90351DiVA: diva2:612957
Conference
Reglermöte 2004, Göteborg, Sweden, May, 2004
Available from: 2013-03-25 Created: 2013-03-24 Last updated: 2013-03-25

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Schön, ThomasGustafsson, Fredrik

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