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Linear Dynamic Network Reconstruction from Heterogeneous Datasets
Univ Luxembourg, Luxembourg.
Univ Luxembourg, Luxembourg.
Imperial Coll London, England; Imperial Coll London, England.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
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2017 (English)In: IFAC PAPERSONLINE, ELSEVIER SCIENCE BV , 2017, Vol. 50, no 1, p. 10586-10591Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses reconstruction of linear dynamic networks from heterogeneous datasets. Those datasets consist of measurements from linear dynamical systems in multiple experiments subjected to different experimental conditions, e.g., changes/perturbations in parameters, disturbance or noise. A main assumption is that the Boolean structures of the underlying networks are the same in all experiments. The ARMAX model is adopted to parameterize the general linear dynamic network representation "Dynamical Structure Function" (DSF), which provides the Granger Causality graph as a special case. The network identification is performed by integrating all available datasets and promote group sparsity to assure both network sparsity and the consistency of Boolean structures over datasets. In terms of solving the problem, a treatment by the iterative reweighted l(1) method is used, together with its implementations via proximal methods and ADMM for large-dimensional networks. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2017. Vol. 50, no 1, p. 10586-10591
Series
IFAC Papers online, E-ISSN 2405-8963
Keywords [en]
system identification; dynamic network reconstruction; heterogeneous datasets
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-145856DOI: 10.1016/j.ifacol.2017.08.1314ISI: 000423965100257OAI: oai:DiVA.org:liu-145856DiVA, id: diva2:1192121
Conference
20th World Congress of the International-Federation-of-Automatic-Control (IFAC)
Note

Funding Agencies|Fonds National de la Recherche Luxembourg [AFR-9247977]

Available from: 2018-03-21 Created: 2018-03-21 Last updated: 2018-03-21

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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