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)

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.

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@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.},
  added-at = {2022-03-31T09:31:59.000+0200},
  address = {Berlin, Germany},
  author = {Kahl, Matthias and Kroll, Andreas},
  biburl = {https://puma.uni-kassel.de/bibtex/27bddbf69780b0f1b0a555f62ef0c7b73/00987301},
  booktitle = {21th IFAC World Congress},
  interhash = {e39dcde9cad061ebab140c7a19ad184c},
  intrahash = {7bddbf69780b0f1b0a555f62ef0c7b73},
  journal = {IFAC-PapersOnLine},
  keywords = {imported},
  language = {english},
  mrtnote = {peer,SFS_TS},
  number = 2,
  organization = {IFAC},
  owner = {duerrbaum},
  pages = {1182--1187},
  publisher = {Elsevier},
  timestamp = {2022-03-31T09:42:48.000+0200},
  title = {Extending Regularized Least Squares Support Vector Machines for Order Selection of Dynamical Takagi-Sugeno Models},
  volume = 53,
  year = 2020
}

%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.

PUMA

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)

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Extending Regularized Least Squares Support Vector Machines for Order Selection of Dynamical Takagi-Sugeno ModelsM. Kahl, und A. Kroll. 21th IFAC World Congress, 53, Seite 1182--1187. Berlin, Germany, IFAC, Elsevier, (2020)

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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)