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A new kernel-based approach to system identification with quantized output data
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9368-3079
2017 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 85, 145-152 p.Article in journal (Refereed) Published
Abstract [en]

In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. This serves as a starting point to cast our system identification problem into a Bayesian framework. We employ Markov Chain Monte Carlo methods to provide an estimate of the system. In particular, we design two methods based on the so-called Gibbs sampler that allow also to estimate the kernel hyperparameters by marginal likelihood maximization via the expectation-maximization method. Numerical simulations show the effectiveness of the proposed scheme, as compared to the state-of-the-art kernel-based methods when these are employed in system identification with quantized data. (C) 2017 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
2017. Vol. 85, 145-152 p.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-218225DOI: 10.1016/j.automatica.2017.07.053ISI: 000414818100016Scopus ID: 2-s2.0-85027880897OAI: oai:DiVA.org:kth-218225DiVA: diva2:1160816
Note

QC 20171128

Available from: 2017-11-28 Created: 2017-11-28 Last updated: 2017-11-28Bibliographically approved

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Hjalmarsson, Håkan

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