Extending Regularized Least Squares Support Vector Machines for Order Selection of Dynamical Takagi-Sugeno Models
M. Kahl, und A. Kroll. 21th IFAC World Congress, 53, Seite 1182--1187. Berlin, Germany, IFAC, Elsevier, (2020)
Zusammenfassung
In this paper, the problem of order selection for nonlinear dynamical Takagi-Sugeno (TS) fuzzy models is adressed. It is solved by reformulating the TS model in its Linear Parameter Varying (LPV) form and applying an extension of a recently proposed Regularized Least Squares Support Vector Machine (R-LSSVM) technique for LPV models. For that, a nonparametric formulation of the TS identi
cation problem is proposed which uses data-dependent basis functions. By doing so, the partition of unity of the TS model is preserved and the scheduling dependencies of the model are obtained in a nonparametric manner. For the local order selection, a regularization approach is used which forces the coeffcient functions of insignifcant values of the lagged input and output towards zero.
