Input selection in N2SID using group lasso regularization

M. Klingspor; A Hansson; J. Löfberg; Michel Verhaegen

doi:10.1016/j.ifacol.2017.08.1472

Input selection in N2SID using group lasso regularization

M. Klingspor, A Hansson, J. Löfberg, Michel Verhaegen

Team Raf Van de Plas

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

Input selection is an important and oftentimes difficult challenge in system identification. In order to achieve less complex models, irrelevant inputs should be methodically and correctly discarded before or under the estimation process. In this paper we introduce a novel method of input selection that is carried out as a natural extension in a subspace method. We show that the method robustly and accurately performs input selection at various noise levels and that it provides good model estimates.

Original language	English
Title of host publication	IFAC-PapersOnLine
Subtitle of host publication	Proceedings 20th IFAC World Congress
Editors	Denis Dochain, Didier Henrion, Dimitri Peaucelle
Place of Publication	Laxenburg, Austria
Publisher	Elsevier
Pages	9474-9479
Volume	50-1
DOIs	https://doi.org/10.1016/j.ifacol.2017.08.1472
Publication status	Published - 2017
Event	20th World Congress of the International Federation of Automatic Control (IFAC), 2017 - Toulouse, France Duration: 9 Jul 2017 → 14 Jul 2017 Conference number: 20 https://www.ifac2017.org

Publication series

Name	IFAC-PapersOnline
Number	1
Volume	50
ISSN (Print)	2405-8963

Conference

Conference	20th World Congress of the International Federation of Automatic Control (IFAC), 2017
Abbreviated title	IFAC 2017
Country/Territory	France
City	Toulouse
Period	9/07/17 → 14/07/17
Internet address	https://www.ifac2017.org

Keywords

Input selection
System identification
State-space models
N2SID
Subspace methods
Signal-to-noise ratio

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10.1016/j.ifacol.2017.08.1472

1-s2.0-S2405896317320487-mainFinal published version, 482 KB

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title = "Input selection in N2SID using group lasso regularization",

abstract = "Input selection is an important and oftentimes difficult challenge in system identification. In order to achieve less complex models, irrelevant inputs should be methodically and correctly discarded before or under the estimation process. In this paper we introduce a novel method of input selection that is carried out as a natural extension in a subspace method. We show that the method robustly and accurately performs input selection at various noise levels and that it provides good model estimates.",

keywords = "Input selection, System identification, State-space models, N2SID, Subspace methods, Signal-to-noise ratio",

author = "M. Klingspor and A Hansson and J. L{\"o}fberg and Michel Verhaegen",

year = "2017",

doi = "10.1016/j.ifacol.2017.08.1472",

language = "English",

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editor = "Denis Dochain and Didier Henrion and Dimitri Peaucelle",

booktitle = "IFAC-PapersOnLine",

note = "20th World Congress of the International Federation of Automatic Control (IFAC), 2017, IFAC 2017 ; Conference date: 09-07-2017 Through 14-07-2017",

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Klingspor, M, Hansson, A, Löfberg, J & Verhaegen, M 2017, Input selection in N2SID using group lasso regularization. in D Dochain, D Henrion & D Peaucelle (eds), IFAC-PapersOnLine: Proceedings 20th IFAC World Congress. vol. 50-1, IFAC-PapersOnline, no. 1, vol. 50, Elsevier, Laxenburg, Austria, pp. 9474-9479, 20th World Congress of the International Federation of Automatic Control (IFAC), 2017, Toulouse, France, 9/07/17. https://doi.org/10.1016/j.ifacol.2017.08.1472

Input selection in N2SID using group lasso regularization. / Klingspor, M.; Hansson, A; Löfberg, J. et al.
IFAC-PapersOnLine: Proceedings 20th IFAC World Congress. ed. / Denis Dochain; Didier Henrion; Dimitri Peaucelle. Vol. 50-1 Laxenburg, Austria: Elsevier, 2017. p. 9474-9479 (IFAC-PapersOnline; Vol. 50, No. 1).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Hansson, A

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AU - Verhaegen, Michel

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PY - 2017

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N2 - Input selection is an important and oftentimes difficult challenge in system identification. In order to achieve less complex models, irrelevant inputs should be methodically and correctly discarded before or under the estimation process. In this paper we introduce a novel method of input selection that is carried out as a natural extension in a subspace method. We show that the method robustly and accurately performs input selection at various noise levels and that it provides good model estimates.

AB - Input selection is an important and oftentimes difficult challenge in system identification. In order to achieve less complex models, irrelevant inputs should be methodically and correctly discarded before or under the estimation process. In this paper we introduce a novel method of input selection that is carried out as a natural extension in a subspace method. We show that the method robustly and accurately performs input selection at various noise levels and that it provides good model estimates.

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KW - System identification

KW - State-space models

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KW - Subspace methods

KW - Signal-to-noise ratio

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M3 - Conference contribution

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A2 - Dochain, Denis

A2 - Henrion, Didier

A2 - Peaucelle, Dimitri

PB - Elsevier

CY - Laxenburg, Austria

T2 - 20th World Congress of the International Federation of Automatic Control (IFAC), 2017

Y2 - 9 July 2017 through 14 July 2017

ER -

Input selection in N2SID using group lasso regularization

Abstract

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Keywords

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