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Identification of Mixed Linear/Nonlinear State-Space 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.
2010 (English)In: Proceedings of the 49th IEEE Conference on Decision and Control, 2010, 6377-6382 p.Conference paper, Published paper (Refereed)
Abstract [en]

The primary contribution of this paper is an algorithm capable of identifying parameters in certain mixed linear/nonlinear state-space models, containing conditionally linear Gaussian substructures. More specifically, we employ the standard maximum likelihood framework and derive an expectation maximization type algorithm. This involves a nonlinear smoothing problem for the state variables, which for the conditionally linear Gaussian system can be efficiently solved using a so called Rao-Blackwellized particle smoother (RBPS). As a secondary contribution of this paper we extend an existing RBPS to be able to handle the fully interconnected model under study.

 

Place, publisher, year, edition, pages
2010. 6377-6382 p.
Keyword [en]
Nonlinear system identification, Expectation maximization, Particle smoothing, Rao-Blackwellization
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-63502DOI: 10.1109/CDC.2010.5717191ISBN: 978-1-4244-7745-6 (print)OAI: oai:DiVA.org:liu-63502DiVA: diva2:380236
Conference
49th IEEE Conference on Decision and Control, Atlanta, GA, USA, 15-17 December, 2010
Note

©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Fredrik Lindsten and Thomas B. Schön, Identification of Mixed Linear/Nonlinear State-Space Models, 2010, Proceedings of the 49th IEEE Conference on Decision and Control (CDC), 6377-6382.

Available from: 2010-12-22 Created: 2010-12-20 Last updated: 2013-07-09Bibliographically approved

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Lindsten, FredrikSchön, Thomas

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