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Model Structure Selection - An Update
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9368-3079
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-0355-2663
2014 (English)In: 2014 European Control Conference (ECC), IEEE , 2014, 2382-2385 p.Conference paper, Published paper (Refereed)
Abstract [en]

While the topic has a long history in research, model structure selection is still one of the more challenging problems in system identification. In this tutorial we focus on impulse response modelling, and link classical techniques such as hypothesis testing and information criteria (e.g. AIC) to recent model estimation approaches, including regularisation. We discuss the problem from minimum mean-square error and maximum-likelihood perspectives.

Place, publisher, year, edition, pages
IEEE , 2014. 2382-2385 p.
Keyword [en]
Impulse response, Impulse testing, Maximum likelihood, Classical techniques, Hypothesis testing, Information criterion, Minimum mean-square error, Model estimation, Model structure selection, Regularisation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-163502DOI: 10.1109/ECC.2014.6862639ISI: 000349955702114Scopus ID: 2-s2.0-84911465723ISBN: 978-3-9524269-1-3 (print)OAI: oai:DiVA.org:kth-163502DiVA: diva2:801078
Conference
13th European Control Conference, ECC 2014, Strasbourg Convention and Exhibition Center Place de Bordeaux Strasbourg, France, 24 June 2014 through 27 June 2014
Note

QC 20150408

Available from: 2015-04-08 Created: 2015-04-07 Last updated: 2015-04-08Bibliographically approved

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Hjalmarsson, HåkanRojas, Cristian R.

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • de-DE
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  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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