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Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations
Uppsala universitet, Avdelningen för systemteknik, Sweden.
Uppsala universitet, Avdelningen för systemteknik, Sweden.
Uppsala universitet, Avdelningen för systemteknik, Sweden.ORCID iD: 0000-0001-5183-234X
2018 (English)In: 18th IFAC Symposium on System IdentificationSYSID 2018 Proceedings, Elsevier, 2018, p. 652-657Conference paper, Published paper (Refereed)
Abstract [en]

When classical particle filtering algorithms are used for maximum likelihood parameter estimation in nonlinear state-space models, a key challenge is that estimates of the likelihood function and its derivatives are inherently noisy. The key idea in this paper is to run a particle filter based on a current parameter estimate, but then use the output from this particle filter to re-evaluate the likelihood function approximation also for other parameter values. This results in a (local) deterministic approximation of the likelihood and any standard optimization routine can be applied to find the maximum of this approximation. By iterating this procedure we eventually arrive at a final parameter estimate.

Place, publisher, year, edition, pages
Elsevier, 2018. p. 652-657
Series
IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
National Category
Signal Processing Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-159807DOI: 10.1016/j.ifacol.2018.09.216ISI: 000446599200111OAI: oai:DiVA.org:liu-159807DiVA, id: diva2:1344821
Conference
SYSID 2018, July 9–11, Stockholm, Sweden
Funder
Swedish Foundation for Strategic Research , RT15-0012, ICA16-0015Swedish Research Council, 621-2016-06079, 2016-04278Available from: 2018-10-08 Created: 2019-08-22 Last updated: 2019-08-23

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Svensson, AndreasLindsten, FredrikSchön, Thomas B.

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf