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Kernel-based system identification from noisy and incomplete intput-output data
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification)ORCID iD: 0000-0002-2831-2909
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9368-3079
2016 (English)In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016, IEEE conference proceedings, 2016, p. 2061-2066Conference paper, Published paper (Refereed)
Abstract [en]

In this contribution, we propose a kernel-based method for the identification of linear systems from noisy and incomplete input-output datasets. We model the impulse response of the system as a Gaussian process whose covariance matrix is given by the recently introduced stable spline kernel. We adopt an empirical Bayes approach to estimate the posterior distribution of the impulse response given the data. The noiseless and missing data samples, together with the kernel hyperparameters, are estimated maximizing the joint marginal likelihood of the input and output measurements. To compute the marginal-likelihood maximizer, we build a solution scheme based on the Expectation-Maximization method. Simulations on a benchmark dataset show the effectiveness of the method.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. p. 2061-2066
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering; Information and Communication Technology
Identifiers
URN: urn:nbn:se:kth:diva-199882DOI: 10.1109/CDC.2016.7798567ISI: 000400048102039Scopus ID: 2-s2.0-85010739057ISBN: 978-1-5090-1837-6 (print)OAI: oai:DiVA.org:kth-199882DiVA, id: diva2:1065900
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
Note

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

Available from: 2017-01-17 Created: 2017-01-17 Last updated: 2017-06-12Bibliographically approved

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