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Evaluation of methods for feasible parameter set estimation of Takagi-Sugeno models for nonlinear regression with bounded errors

Evaluation of methods for feasible parameter set estimation of Takagi-Sugeno models for nonlinear... AbstractIn 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png at - Automatisierungstechnik de Gruyter

Evaluation of methods for feasible parameter set estimation of Takagi-Sugeno models for nonlinear regression with bounded errors

at - Automatisierungstechnik , Volume 69 (10): 12 – Oct 26, 2021

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Publisher
de Gruyter
Copyright
© 2021 Walter de Gruyter GmbH, Berlin/Boston
ISSN
2196-677X
eISSN
2196-677X
DOI
10.1515/auto-2020-0157
Publisher site
See Article on Publisher Site

Abstract

AbstractIn 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.

Journal

at - Automatisierungstechnikde Gruyter

Published: Oct 26, 2021

Keywords: Takagi-Sugeno model; parameter estimation; uncertainty; nonlinear regression; Takagi-Sugeno-Modell; Parameterschätzung; Unsicherheit; Nichtlineare Regression

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