Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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
Simulated Pseudo Maximum Likelihood Identification of Nonlinear Models
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH Royal Institute of Technology. (System Identification)ORCID iD: 0000-0001-5474-7060
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification)ORCID iD: 0000-0002-9368-3079
2017 (English)In: The 20th IFAC World Congress, Elsevier, 2017, Vol. 50, p. 14058-14063Conference paper, Published paper (Refereed)
Abstract [en]

Nonlinear stochastic parametric models are widely used in various fields. However, for these models, the problem of maximum likelihood identification is very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the analytically intractable likelihood function and compute either the maximum likelihood or a Bayesian estimator. These methods, albeit asymptotically optimal, are computationally expensive. In this contribution, we present a simulation-based pseudo likelihood estimator for nonlinear stochastic models. It relies only on the first two moments of the model, which are easy to approximate using Monte-Carlo simulations on the model. The resulting estimator is consistent and asymptotically normal. We show that the pseudo maximum likelihood estimator, based on a multivariate normal family, solves a prediction error minimization problem using a parameterized norm and an implicit linear predictor. In the light of this interpretation, we compare with the predictor defined by an ensemble Kalman filter. Although not identical, simulations indicate a close relationship. The performance of the simulated pseudo maximum likelihood method is illustrated in three examples. They include a challenging state-space model of dimension 100 with one output and 2 unknown parameters, as well as an application-motivated model with 5 states, 2 outputs and 5 unknown parameters.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 50, p. 14058-14063
Series
IFAC-PapersOnLine
Keywords [en]
System identification, Nonlinear systems, Stochastic systems, Monte Carlo method
National Category
Control Engineering Signal Processing
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-216419DOI: 10.1016/j.ifacol.2017.08.1841Scopus ID: 2-s2.0-85044304531OAI: oai:DiVA.org:kth-216419DiVA, id: diva2:1151111
Conference
The 20th IFAC World Congress
Note

QC 20171024

Available from: 2017-10-22 Created: 2017-10-22 Last updated: 2017-12-04Bibliographically approved

Open Access in DiVA

fulltext(308 kB)49 downloads
File information
File name FULLTEXT01.pdfFile size 308 kBChecksum SHA-512
585e3d7ddef2171a0107057a9cf5e44fc8e1c0272e8a05dbd87b53ba70c620fac118b47a628440205d0a4b6306f60d9e6d86877b399e31c82ea48bf09e81118a
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopushttps://www.sciencedirect.com/science/article/pii/S2405896317324655

Search in DiVA

By author/editor
Abdalmoaty, MohamedHjalmarsson, Håkan
By organisation
Automatic ControlACCESS Linnaeus Centre
Control EngineeringSignal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 49 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 562 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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