Evaluation of methods for feasible parameter set estimation of Takagi-Sugeno models for nonlinear regression with bounded errors
F. Wittich, und A. Kroll.
at -- Automatisierungstechnik 69 (10): 836--847 (2021)

In data-driven modeling besides the point estimate of the model parameters, an estimation of the parameter uncertainty is of great interest. For this, bounded error parameter estimation methods can be used. These are particularly interesting for problems where the stochastical properties of the random effects are unknown and cannot be determined. In this paper, different methods for obtaining a feasible parameter set are evaluated for the use with Takagi-Sugeno models. Case studies with simulated data and with measured data from a manufacturing process are presented.

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@article{2021-FW-at-BE_TS_Methods,
  abstract = {In data-driven modeling besides the point estimate of the model parameters, an estimation of the parameter uncertainty is of great interest. For this, bounded error parameter estimation methods can be used. These are particularly interesting for problems where the stochastical properties of the random effects are unknown and cannot be determined. In this paper, different methods for obtaining a feasible parameter set are evaluated for the use with Takagi-Sugeno models. Case studies with simulated data and with measured data from a manufacturing process are presented.},
  added-at = {2022-03-31T09:31:59.000+0200},
  author = {Wittich, Felix and Kroll, Andreas},
  biburl = {https://puma.uni-kassel.de/bibtex/2b2b4475dbb95dec665c88871e9b9daf1/00987301},
  interhash = {699264d975cd4660d59e239eb78b00eb},
  intrahash = {b2b4475dbb95dec665c88871e9b9daf1},
  journal = {at -- Automatisierungstechnik},
  journaltitle = {Computational Intelligence},
  keywords = {isac-www mrt,},
  language = {english},
  mrtnote = {peer,HartDrehen},
  number = 10,
  owner = {wittich},
  pages = {836--847},
  timestamp = {2022-06-23T11:44:42.000+0200},
  title = {Evaluation of methods for feasible parameter set estimation of Takagi-Sugeno models for nonlinear regression with bounded errors},
  url = {https://www.degruyter.com/document/doi/10.1515/auto-2020-0157/html},
  volume = 69,
  year = 2021
}

%0 Journal Article
%1 2021-FW-at-BE_TS_Methods
%A Wittich, Felix
%A Kroll, Andreas
%D 2021
%J at -- Automatisierungstechnik
%K isac-www mrt,
%N 10
%P 836--847
%T Evaluation of methods for feasible parameter set estimation of Takagi-Sugeno models for nonlinear regression with bounded errors
%U https://www.degruyter.com/document/doi/10.1515/auto-2020-0157/html
%V 69
%X In data-driven modeling besides the point estimate of the model parameters, an estimation of the parameter uncertainty is of great interest. For this, bounded error parameter estimation methods can be used. These are particularly interesting for problems where the stochastical properties of the random effects are unknown and cannot be determined. In this paper, different methods for obtaining a feasible parameter set are evaluated for the use with Takagi-Sugeno models. Case studies with simulated data and with measured data from a manufacturing process are presented.

PUMA

Evaluation of methods for feasible parameter set estimation of Takagi-Sugeno models for nonlinear regression with bounded errors
F. Wittich, und A. Kroll.
at -- Automatisierungstechnik 69 (10): 836--847 (2021)

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PUMA

Evaluation of methods for feasible parameter set estimation of Takagi-Sugeno models for nonlinear regression with bounded errorsF. Wittich, und A. Kroll. at -- Automatisierungstechnik 69 (10): 836--847 (2021)

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Evaluation of methods for feasible parameter set estimation of Takagi-Sugeno models for nonlinear regression with bounded errors
F. Wittich, und A. Kroll.
at -- Automatisierungstechnik 69 (10): 836--847 (2021)