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Sequential Monte Carlo Methods for System Identification
Department of Information Technology, Uppsala University..
Department of Engineering, University of Cambridge.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-9424-1272
Department of Information Technology, Uppsala University..
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2015 (English)In: Proceedings of the 17th IFAC Symposium on System Identification., 2015, Vol. 48, 775-786 p.Conference paper (Refereed)
Abstract [en]

One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced more than two decades ago), provide numerical solutions to the nonlinear state estimation problems arising in SSMs. When combined with additional identification techniques, these algorithms provide solid solutions to the nonlinear system identification problem. We describe two general strategies for creating such combinations and discuss why SMC is a natural tool for implementing these strategies.

Place, publisher, year, edition, pages
2015. Vol. 48, 775-786 p.
Keyword [en]
Nonlinear system identification; nonlinear state space model; particle filter; particle smoother; sequential Monte Carlo; MCMC
National Category
Control Engineering Computational Mathematics
URN: urn:nbn:se:liu:diva-123667DOI: 10.1016/j.ifacol.2015.12.224OAI: diva2:891387
Proceedings of the 17th IFAC Symposium on System Identification, Beijing, China, October 19-21, 2015.
Swedish Research Council, 637-2014-466Swedish Research Council, 621-2013-5524
Available from: 2016-01-07 Created: 2016-01-07 Last updated: 2016-03-10

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Dahlin, JohanAndersson Naesseth, Christian
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Automatic ControlFaculty of Science & Engineering
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