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Sparse Estimation of Polynomial and Rational Dynamical Models
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-0355-2663
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9368-3079
2014 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 59, no 11, 2962-2977 p.Article in journal (Refereed) Published
Abstract [en]

In many practical situations, it is highly desirable to estimate an accurate mathematical model of a real system using as few parameters as possible. At the same time, the need for an accurate description of the system behavior without knowing its complete dynamical structure often leads to model parameterizations describing a rich set of possible hypotheses; an unavoidable choice, which suggests sparsity of the desired parameter estimate. An elegant way to impose this expectation of sparsity is to estimate the parameters by penalizing the criterion with the l(0) "norm" of the parameters. Due to the non-convex nature of the l(0)-norm, this penalization is often implemented as solving an optimization program based on a convex relaxation (e. g., l(1)/LASSO, nuclear norm, ...). Two difficulties arise when trying to apply these methods: (1) the need to use cross-validation or some related technique for choosing the values of regularization parameters associated with the l(1) penalty; and (2) the requirement that the (unpenalized) cost function must be convex. To address the first issue, we propose a new technique for sparse linear regression called SPARSEVA, with close ties with the LASSO (least absolute shrinkage and selection operator), which provides an automatic tuning of the amount of regularization. The second difficulty, which imposes a severe constraint on the types of model structures or estimation methods on which the l(1) relaxation can be applied, is addressed by combining SPARSEVA and the Steiglitz-McBride method. To demonstrate the advantages of the proposed approach, a solid theoretical analysis and an extensive simulation study are provided.

Place, publisher, year, edition, pages
2014. Vol. 59, no 11, 2962-2977 p.
Keyword [en]
AIC, BIC, cross-validation, LASSO, model structure selection, sparse estimation, Steiglitz-McBride method, system identification
National Category
Control Engineering Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-157602DOI: 10.1109/TAC.2014.2351711ISI: 000344482500009Scopus ID: 2-s2.0-84908408937OAI: oai:DiVA.org:kth-157602DiVA: diva2:771254
Funder
EU, FP7, Seventh Framework Programme, 267381
Note

QC 20141212

Available from: 2014-12-12 Created: 2014-12-11 Last updated: 2017-12-05Bibliographically approved

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Rojas, Cristian R.Hjalmarsson, Håkan

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