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Sequential Monte Ca...
Sequential Monte Carlo Methods for System Identification
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- Schön, Thomas Bo (author)
- Department of Information Technology, Uppsala University
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- Lindsten, Fredrik, 1984- (author)
- Department of Engineering, University of Cambridge, UK
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- Dahlin, Johan, 1986- (author)
- Linköpings universitet,Reglerteknik,Tekniska fakulteten
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- Wågberg, Johan (author)
- Department of Information Technology, Uppsala University
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- Andersson Naesseth, Christian (author)
- Linköpings universitet,Reglerteknik,Tekniska fakulteten
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- Svensson, Andreas (author)
- Department of Information Technology, Uppsala University
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- Dai, Liang (author)
- Department of Information Technology, Uppsala University
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(creator_code:org_t)
- Elsevier, 2015
- 2015
- English.
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In: Proceedings of the 17th IFAC Symposium on System Identification.. - : Elsevier. ; , s. 775-786
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https://liu.diva-por... (primary) (Raw object)
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https://doi.org/10.1...
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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
Publication and Content Type
- ref (subject category)
- kon (subject category)
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