DC FieldValueLanguage
dc.contributor.authorDatar, Adwait-
dc.contributor.authorSchulz, Erik-
dc.contributor.authorWerner, Herbert-
dc.date.accessioned2019-04-25T13:13:22Z-
dc.date.available2019-04-25T13:13:22Z-
dc.date.issued2018-08-09-
dc.identifier.citationAmerican Control Conference (2018-June): 2011-2016 (2018-08-09)de_DE
dc.identifier.isbn978-153865428-6de_DE
dc.identifier.issn0743-1619de_DE
dc.identifier.urihttp://hdl.handle.net/11420/2491-
dc.description.abstractThe bias-variance trade-off is very sensitive to the prior selection of functional dependencies when identifying linear parameter-varying (LPV) systems. To counteract this difficulty, various non-parametric methods have been recently proposed. These methods avoid the manual selection of the functional dependency but rather learn it during the identification itself. In this paper, we propose an algorithm to implement the ϵ-tube support vector regression (SVR) approach to such an LPV identification problem. We use some results from the machine learning literature to tune the parameters and provide different directions one could take to optimize these parameters. We demonstrate the effectiveness of our method on an example and compare the results with other methods recently proposed. We observe that because of the insensitive ϵ-tube, the number of parameters was greatly reduced still maintaining the same accuracy in terms of the best fit ratio.en
dc.language.isoende_DE
dc.relation.ispartofProceedings of the American Control Conferencede_DE
dc.titleIdentification of Linear Parameter-Varying Models with Unknown Parameter Dependence Using ϵ-Support Vector Regressionde_DE
dc.typeinProceedingsde_DE
dc.type.dinicontributionToPeriodical-
dcterms.DCMITypeText-
tuhh.abstract.englishThe bias-variance trade-off is very sensitive to the prior selection of functional dependencies when identifying linear parameter-varying (LPV) systems. To counteract this difficulty, various non-parametric methods have been recently proposed. These methods avoid the manual selection of the functional dependency but rather learn it during the identification itself. In this paper, we propose an algorithm to implement the ϵ-tube support vector regression (SVR) approach to such an LPV identification problem. We use some results from the machine learning literature to tune the parameters and provide different directions one could take to optimize these parameters. We demonstrate the effectiveness of our method on an example and compare the results with other methods recently proposed. We observe that because of the insensitive ϵ-tube, the number of parameters was greatly reduced still maintaining the same accuracy in terms of the best fit ratio.de_DE
tuhh.publisher.doi10.23919/ACC.2018.8431736-
tuhh.publication.instituteRegelungstechnik E-14de_DE
tuhh.type.opusInProceedings (Aufsatz / Paper einer Konferenz etc.)-
tuhh.institute.germanE-14de
tuhh.institute.englishRegelungstechnik E-14de_DE
tuhh.gvk.hasppnfalse-
dc.type.drivercontributionToPeriodical-
dc.type.casraiConference Paper-
tuhh.container.startpage2011de_DE
tuhh.container.endpage2016de_DE
dc.relation.conferenceAmerican Control Conference, ACC 2018de_DE
dc.relation.projectLPV System Identification-
item.creatorOrcidDatar, Adwait-
item.creatorOrcidSchulz, Erik-
item.creatorOrcidWerner, Herbert-
item.languageiso639-1en-
item.openairetypeinProceedings-
item.fulltextNo Fulltext-
item.creatorGNDDatar, Adwait-
item.creatorGNDSchulz, Erik-
item.creatorGNDWerner, Herbert-
item.mappedtypeinProceedings-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.grantfulltextnone-
item.cerifentitytypePublications-
crisitem.project.funderTechnische Universität Hamburg-
crisitem.project.funderrorid04bs1pb34-
crisitem.author.deptRegelungstechnik E-14-
crisitem.author.deptRegelungstechnik E-14-
crisitem.author.orcid0000-0003-3456-5539-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
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