@inproceedings{Kahl-IFAC-2020,
  abstract = {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.},
  address = {Berlin, Germany},
  author = {Kahl, Matthias and Kroll, Andreas},
  booktitle = {21th IFAC World Congress},
  interhash = {e39dcde9cad061ebab140c7a19ad184c},
  intrahash = {7bddbf69780b0f1b0a555f62ef0c7b73},
  journal = {IFAC-PapersOnLine},
  language = {english},
  mrtnote = {peer,SFS_TS},
  number = 2,
  organization = {IFAC},
  owner = {duerrbaum},
  pages = {1182--1187},
  publisher = {Elsevier},
  title = {Extending Regularized Least Squares Support Vector Machines for Order Selection of Dynamical Takagi-Sugeno Models},
  volume = 53,
  year = 2020
}