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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 (engelsk)Inngår i: 18th IFAC Symposium on System IdentificationSYSID 2018 Proceedings, Elsevier, 2018, s. 652-657Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Elsevier, 2018. s. 652-657
Serie
IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-159807DOI: 10.1016/j.ifacol.2018.09.216ISI: 000446599200111OAI: oai:DiVA.org:liu-159807DiVA, id: diva2:1344821
Konferanse
SYSID 2018, July 9–11, Stockholm, Sweden
Forskningsfinansiär
Swedish Foundation for Strategic Research , RT15-0012, ICA16-0015Swedish Research Council, 621-2016-06079, 2016-04278Tilgjengelig fra: 2018-10-08 Laget: 2019-08-22 Sist oppdatert: 2019-08-23

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Svensson, AndreasLindsten, FredrikSchön, Thomas B.

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