%0 Conference Paper
%1 Kahl-IFAC-2020
%A Kahl, Matthias
%A Kroll, Andreas
%B 21th IFAC World Congress
%C Berlin, Germany
%D 2020
%I Elsevier
%J IFAC-PapersOnLine
%K imported
%N 2
%P 1182--1187
%T Extending Regularized Least Squares Support Vector Machines for Order Selection of Dynamical Takagi-Sugeno Models
%V 53
%X 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.