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Identification and prediction in dynamic networks with unobservable nodes
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-3498-3204
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-6523-8499
2017 (English)In: Proceedings of the 20th IFAC World Congress, Toulouse, France, July 9-14, 2017, , 18 p.Conference paper, Oral presentation only (Refereed)
Abstract [en]

The interest for system identification in dynamic networks has increased recently with a wide variety of applications. In many cases, it is intractable or undesirable to observe all nodes in a network and thus, to estimate the complete dynamics. Furthermore, it might even be challenging to estimate a subset of the network if key nodes are unobservable due to correlation between the nodes. In this contribution, we will discuss an approach to treat this problem. The approach relies on additional measurements that are dependent on the unobservable nodes and thus indirectly contain information about them. These measurements are used to form an alternative indirect model that is only dependent on observed nodes. The purpose of estimating this indirect model can be either to recover information about modules in the original network or to make accurate predictions of variables in the network. Examples are provided for both recovery of the original modules and prediction of nodes.

Place, publisher, year, edition, pages
2017. , 18 p.
Keyword [en]
Dynamic networks, closed-loop identification, identifiability, system identification
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-139365OAI: oai:DiVA.org:liu-139365DiVA: diva2:1126191
Conference
20th IFAC World Congress, Toulouse, France, July 9-14
Available from: 2017-07-13 Created: 2017-07-13 Last updated: 2017-07-13

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Output format
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  • asciidoc
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