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Sequential Monte Carlo Methods for System Identification

Schön, Thomas Bo (author)
Department of Information Technology, Uppsala University
Lindsten, Fredrik, 1984- (author)
Department of Engineering, University of Cambridge, UK
Dahlin, Johan, 1986- (author)
Linköpings universitet,Reglerteknik,Tekniska fakulteten
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Wågberg, Johan (author)
Department of Information Technology, Uppsala University
Andersson Naesseth, Christian (author)
Linköpings universitet,Reglerteknik,Tekniska fakulteten
Svensson, Andreas (author)
Department of Information Technology, Uppsala University
Dai, Liang (author)
Department of Information Technology, Uppsala University
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 (creator_code:org_t)
Elsevier, 2015
2015
English.
In: Proceedings of the 17th IFAC Symposium on System Identification.. - : Elsevier. ; , s. 775-786
  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
NATURVETENSKAP  -- Matematik -- Beräkningsmatematik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Computational Mathematics (hsv//eng)

Keyword

Nonlinear system identification; nonlinear state space model; particle filter; particle smoother; sequential Monte Carlo; MCMC

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