Evaluation of methods for feasible parameter set estimation of Takagi-Sugeno models for nonlinear regression with bounded errors-Reference-Cited by-同舟云学术

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

Published:2021-10-01 Issue:10 Volume:69 Page:836-847
ISSN:2196-677X
Container-title:at - Automatisierungstechnik
language:en
Short-container-title:

Author:

Wittich Felix¹,Kroll Andreas¹

Affiliation:

1. Department of Measurement and Control, Institute for System Analytics and Control, Faculty of Mechanical Engineering , University of Kassel , Kassel , Germany

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

https://www.degruyter.com/document/doi/10.1515/auto-2020-0157/pdf

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