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A fully Bayesian approach to kernel-based regularization for impulse response estimation⁎
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0003-0355-2663
2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 186-191Article in journal (Refereed) Published
Abstract [en]

Kernel-based regularization has recently been shown to be a successful method for impulse response estimation. This technique usually requires choosing a vector of hyper-parameters in order to form an appropriate regularization matrix. In this paper, we develop an alternative way to obtain kernel-based regularization estimates by Bayesian model mixing. This new approach is tested against state-of-the-art methods for hyperparameter tuning in regularized FIR estimation, with favorable results in many cases.

Place, publisher, year, edition, pages
Elsevier B.V. , 2018. Vol. 51, no 15, p. 186-191
Keywords [en]
Bayesian estimation, kernel-based regularization, Linear system identification, model mixing, Impulse response, Linear systems, Mixing, Bayesian estimations, Bayesian model, Fully bayesian approaches, Hyper-parameter, New approaches, State-of-the-art methods, Bayesian networks
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-247500DOI: 10.1016/j.ifacol.2018.09.123ISI: 000446599200033Scopus ID: 2-s2.0-85054355409OAI: oai:DiVA.org:kth-247500DiVA, id: diva2:1301851
Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-04-03Bibliographically approved

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González, Rodrigo A.Rojas, Cristian R.

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