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Structure Identification of Dynamical Takagi-Sugeno Fuzzy Models by Using LPV Techniques

Kahl, Matthias; Kroll, Andreas

Abstract:
In this paper the problem of order selection for nonlinear dynamical Takagi-Sugeno (TS) fuzzy models is investigated. The problem is solved by formulating the TS model in its Linear Parameter Varying (LPV) form and applying a recently proposed Regularized Least Squares SupportVector Machine (R-LSSVM) technique for LPV models. In contrast to parametric identification approaches, this non-parametric method enables the selection of the model order without specifying the scheduling dependencies of the model coefficients. Once the correct model order is found, a parametric TS model can be re-estimated by standard methods. Different re-estimation approaches are proposed. The approaches are illustrated in a numerical example.

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Verlagsausgabe §
DOI: 10.5445/KSP/1000087327/19
Veröffentlicht am 13.03.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2018
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000117726
Erschienen in Archives of Data Science, Series A (Online First)
Band 5
Heft 1
Seiten A19, 17 S. online
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