OLS identification of network topologies

D. Materassi, G. Innocenti, L. Giarré, M. Salapaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In many applications, it is important to derive information about the topology and the internal connections of more dynamical systems interacting together. Examples can be found in fields as diverse as Economics, Neuroscience and Biochemistry. The paper deals with the problem of deriving a descriptive model of a network, collecting the node outputs as time series with no use of a priori insight on the topology. We cast the problem as the optimization of a cost function where a set of parameters are used to operate a trade-off between accuracy and complexity in the final model. The problem of reducing the complexity is addressed by fixing a certain degree of sparsity and finding the solution that "better" satisfies the constraints according to the criterion of approximation.

Original languageEnglish (US)
Title of host publicationProceedings of the 18th IFAC World Congress
PublisherIFAC Secretariat
Pages8836-8841
Number of pages6
Edition1 PART 1
ISBN (Print)9783902661937
DOIs
StatePublished - Jan 1 2011

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1 PART 1
Volume44
ISSN (Print)1474-6670

Keywords

  • Compressive sensing
  • Identification
  • Networks
  • Reduced models
  • Sparsification

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