Modeling the Drift Function in Stochastic Differential Equations using Reduced Rank Gaussian Processes

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Hostettler, Roland
dc.contributor.author Tronarp, Filip
dc.contributor.author Särkkä, Simo
dc.date.accessioned 2018-12-10T10:27:17Z
dc.date.available 2018-12-10T10:27:17Z
dc.date.issued 2018-01-01
dc.identifier.citation Hostettler , R , Tronarp , F & Särkkä , S 2018 , Modeling the Drift Function in Stochastic Differential Equations using Reduced Rank Gaussian Processes . in 18th IFAC Symposium on System Identification, SYSID 2018 . 15 edn , vol. 51 , IFAC-PapersOnLine , Elsevier , pp. 778-783 , IFAC Symposium on System Identification , Stockholm , Sweden , 09/07/2018 . DOI: 10.1016/j.ifacol.2018.09.137 en
dc.identifier.issn 2405-8963
dc.identifier.other PURE UUID: b296d391-8782-44cf-ba9f-d6dcf145f4c4
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/modeling-the-drift-function-in-stochastic-differential-equations-using-reduced-rank-gaussian-processes(b296d391-8782-44cf-ba9f-d6dcf145f4c4).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85054443004&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/29466683/ELEC_Hostettler_etal_Modeling_the_Drift_IFACPapersOnline_51_15_2018.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/35218
dc.description.abstract In this paper, we propose a Gaussian process-based nonlinear, time-varying drift model for stochastic differential equations. In particular, we combine eigenfunction expansion of the Gaussian process’ covariance kernel in the spatial input variables with spectral decomposition in the time domain to obtain a reduced rank state space representation of the drift model, which avoids the growing complexity (with respect to time) of the full Gaussian process solution. The proposed approach is evaluated on two nonlinear benchmark problems, the Bouc Wen and the cascaded tanks systems. en
dc.format.extent 6
dc.format.extent 778-783
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Elsevier Science Ltd (Pergamon)
dc.relation.ispartof IFAC Symposium on System Identification en
dc.relation.ispartofseries 18th IFAC Symposium on System Identification, SYSID 2018 en
dc.relation.ispartofseries Volume 51, issue 15 en
dc.relation.ispartofseries IFAC-PapersOnLine en
dc.rights openAccess en
dc.subject.other Control and Systems Engineering en
dc.subject.other 111 Mathematics en
dc.title Modeling the Drift Function in Stochastic Differential Equations using Reduced Rank Gaussian Processes en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Electrical Engineering and Automation
dc.subject.keyword Bayesian methods
dc.subject.keyword estimation
dc.subject.keyword filtering
dc.subject.keyword Gaussian processes
dc.subject.keyword Nonlinear system identification
dc.subject.keyword nonparametric methods
dc.subject.keyword smoothing
dc.subject.keyword Control and Systems Engineering
dc.subject.keyword 111 Mathematics
dc.identifier.urn URN:NBN:fi:aalto-201812106233
dc.identifier.doi 10.1016/j.ifacol.2018.09.137
dc.type.version publishedVersion


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