Identification of modules in dynamic networks: An empirical Bayes approach
2016 (English)In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 4612-4617, article id 7798971Conference paper, Published paper (Refereed)
Abstract [en]
We address the problem of identifying a specific module in a dynamic network, 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. Numerical experiments illustrate the effectiveness of the proposed method.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 4612-4617, article id 7798971
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-208567DOI: 10.1109/CDC.2016.7798971ISI: 000400048104129Scopus ID: 2-s2.0-85010796560ISBN: 978-1-5090-1837-6 (print)OAI: oai:DiVA.org:kth-208567DiVA, id: diva2:1108272
Conference
55th IEEE Conference on Decision and Control, CDC 2016, ARIA Resort and Casino, Las Vegas, United States, 12 December 2016 through 14 December 2016
Funder
EU, European Research Council, 267381Swedish Research Council, 621-2009-4017
Note
QC 20170612
2017-06-122017-06-122017-06-12Bibliographically approved