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Spectral analysis of the DC kernel for regularized system identification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-8655-2655
University of Padua, Italy.
University of Padua, Italy.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2015 (English)In: 2015 54TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2015, p. 4017-4022Conference paper, Published paper (Refereed)
Abstract [en]

System identification with regularization methods has attracted increasing attention recently and is a complement to the current standard maximum likelihood/ prediction error method. In this paper, we focus on the kernel-based regularization method and give a spectral analysis of the so-called diagonal correlated (DC) kernel, one family of kernel structures that has been proven useful for linear time-invariant system identification. In particular, using the theory of Bessel functions, we derive the eigenvalues and corresponding eigenfunctions of the DC kernel. Accordingly, we derive the Karhunen-Loeve expansion of the stochastic process whose covariance function is the DC kernel.

Place, publisher, year, edition, pages
IEEE , 2015. p. 4017-4022
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-133148DOI: 10.1109/CDC.2015.7402844ISI: 000381554504033ISBN: 978-1-4799-7886-1 (print)OAI: oai:DiVA.org:liu-133148DiVA, id: diva2:1055171
Conference
54th IEEE Conference on Decision and Control (CDC)
Available from: 2016-12-12 Created: 2016-12-09 Last updated: 2016-12-12

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CiteExportLink to record
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Citation style
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  • nn-NB
  • sv-SE
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Output format
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