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
An empirical Bayes approach to identification of modules in dynamic networks
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands..
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 91, p. 144-151Article in journal (Refereed) Published
Abstract [en]

We present a new method of identifying a specific module in a dynamic network, possibly with feedback loops. Assuming known topology, we express the dynamics by an acyclic network composed of two blocks where the first block accounts for the relation between the known reference signals and the input to the target module, while the second block contains the target module. Using an empirical Bayes approach, we model the first block as a Gaussian vector with covariance matrix (kernel) given by the recently introduced stable spline kernel. The parameters of the target module are estimated by solving a marginal likelihood problem with a novel iterative scheme based on the Expectation-Maximization algorithm. Additionally, we extend the method to include additional measurements downstream of the target module. Using Markov Chain Monte Carlo techniques, it is shown that the same iterative scheme can solve also this formulation. Numerical experiments illustrate the effectiveness of the proposed methods.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2018. Vol. 91, p. 144-151
Keywords [en]
System identification, Dynamic network, Empirical Bayes, Expectation-maximization
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-227753DOI: 10.1016/j.automatica.2018.01.011ISI: 000430756000017Scopus ID: 2-s2.0-85041514402OAI: oai:DiVA.org:kth-227753DiVA, id: diva2:1205692
Funder
Swedish Research Council, 2015-05285 NewLEADS 2016-06079
Note

QC 20180515

Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-05-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Everitt, NiklasHjalmarsson, Håkan
By organisation
ACCESS Linnaeus Centre
In the same journal
Automatica
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 29 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