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dc.contributor.authorKenderi, Gábor*
dc.date.accessioned2021-02-11T21:08:21Z
dc.date.available2021-02-11T21:08:21Z
dc.date.issued2018*
dc.date.submitted2019-07-28 18:37:01*
dc.identifier34226*
dc.identifier.issn16143914*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/54766
dc.description.abstractA nonparametric identification method for highly nonlinear systems is presented that is able to reconstruct the underlying nonlinearities without a priori knowledge of the describing nonlinear functions. The approach is based on nonlinear Kalman Filter algorithms using the well-known state augmentation technique that turns the filter into a dual state and parameter estimator, of which an extension towards nonparametric identification is proposed in the present work.*
dc.languageEnglish*
dc.relation.ispartofseriesSchriftenreihe des Instituts für Technische Mechanik, Karlsruher Institut für Technologie*
dc.subjectT1-995*
dc.subject.othernichtlineare dynamische System*
dc.subject.otherKalman Filter*
dc.subject.othernonlinear dynamic system*
dc.subject.othernonparametric identification*
dc.subject.othernichtparametrische Identifikation*
dc.titleNonparametric identification of nonlinear dynamic systems*
dc.typebook
oapen.identifier.doi10.5445/KSP/1000085419*
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2*
virtual.oapen_relation_isPublishedBy.publisher_nameKIT Scientific Publishing
virtual.oapen_relation_isPublishedBy.publisher_websitehttp://www.ksp.kit.edu/
oapen.relation.isbn9783731508342*
oapen.pagesXXVIII, 194 p.*
oapen.volume32*


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