Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Algorithms and Performance Analysis for Stochastic Wiener System Identification
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0002-1927-1690
2018 (English)In: IEEE Control Systems Letters, ISSN 2475-1456, Vol. 2, no 3, p. 471-476Article in journal (Refereed) Published
Abstract [en]

We analyze the statistical performance of identification of stochastic dynamical systems with non-linear measurement sensors. This includes stochastic Wiener systems, with linear dynamics, process noise and measured by a non-linear sensor with additive measurement noise. There are many possible system identification methods for such systems, including the maximum likelihood (ML) method and the prediction error method. The focus has mostly been on algorithms and implementation, and less is known about the statistical performance and the corresponding Cramér-Rao lower bound (CRLB) for identification of such non-linear systems. We derive expressions for the CRLB and the asymptotic normalized covariance matrix for certain Gaussian approximations of Wiener systems to show how a non-linear sensor affects the accuracy compared to a corresponding linear sensor. The key idea is to take second order statistics into account by using a common parametrization of the mean and the variance of the output process. This analysis also leads to an ML motivated identification method based on the conditional mean predictor and a Gaussian distribution approximation. The analysis is supported by numerical simulations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. Vol. 2, no 3, p. 471-476
Keywords [en]
Nonlinear systems identification, stochastic systems, Covariance matrix, Dynamical systems, Identification (control systems), Linear systems, Maximum likelihood, Nonlinear systems, Religious buildings, Algorithms and performance, Gaussian approximations, Maximum likelihood methods, Prediction error method, Statistical performance, Stochastic dynamical system, System identification methods
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-247211DOI: 10.1109/LCSYS.2018.2840878Scopus ID: 2-s2.0-85057638172OAI: oai:DiVA.org:kth-247211DiVA, id: diva2:1304989
Note

QC 20190415

Available from: 2019-04-15 Created: 2019-04-15 Last updated: 2019-04-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Wahlberg, Bo

Search in DiVA

By author/editor
Wahlberg, Bo
By organisation
Automatic Control
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 4 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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