Data-driven modelling of LTI systems using symbolic regression

Onderzoeksoutput: Bijdrage aan congresAbstractAcademic

Uittreksel

The aim of this project is to automate the task of data-driven
identification of dynamical systems. The underlying goal is
to develop an identification tool that models a physical system
without distinguishing between classes of systems such
as linear, nonlinear or possibly even hybrid systems. Such
an identification tool would be able to mine data generated by the system to infer such structural knowledge, without relying on the expertise of a skilled user. This will allow researchers to shift their focus back from the modelling task to the actual utilization of the model. Such a research objective requires the identification technique to employ tools that are not targeted towards nuanced modelling tasks, but remain applicable for a very broad range of systems. Hence, we seek to develop a new framework for system identification that uses generic tools.

Congres

Congres36th Benelux Meeting on Systems and Control, 28-30 March 2017, Spa, Belgium
LandBelgië
StadSpa
Periode28/03/1730/03/17
Internet adres

Vingerafdruk

Data structures
Identification (control systems)
Hybrid systems
Dynamical systems

Citeer dit

Khandelwal, D., Toth, R., & Van den Hof, P. M. J. (2017). Data-driven modelling of LTI systems using symbolic regression. 145. Abstract van 36th Benelux Meeting on Systems and Control, 28-30 March 2017, Spa, Belgium, Spa, België.
Khandelwal, D. ; Toth, R. ; Van den Hof, P.M.J./ Data-driven modelling of LTI systems using symbolic regression. Abstract van 36th Benelux Meeting on Systems and Control, 28-30 March 2017, Spa, Belgium, Spa, België.
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Data-driven modelling of LTI systems using symbolic regression. / Khandelwal, D.; Toth, R.; Van den Hof, P.M.J.

2017. 145 Abstract van 36th Benelux Meeting on Systems and Control, 28-30 March 2017, Spa, Belgium, Spa, België.

Onderzoeksoutput: Bijdrage aan congresAbstractAcademic

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AU - Toth,R.

AU - Van den Hof,P.M.J.

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N2 - The aim of this project is to automate the task of data-drivenidentification of dynamical systems. The underlying goal isto develop an identification tool that models a physical systemwithout distinguishing between classes of systems suchas linear, nonlinear or possibly even hybrid systems. Suchan identification tool would be able to mine data generated by the system to infer such structural knowledge, without relying on the expertise of a skilled user. This will allow researchers to shift their focus back from the modelling task to the actual utilization of the model. Such a research objective requires the identification technique to employ tools that are not targeted towards nuanced modelling tasks, but remain applicable for a very broad range of systems. Hence, we seek to develop a new framework for system identification that uses generic tools.

AB - The aim of this project is to automate the task of data-drivenidentification of dynamical systems. The underlying goal isto develop an identification tool that models a physical systemwithout distinguishing between classes of systems suchas linear, nonlinear or possibly even hybrid systems. Suchan identification tool would be able to mine data generated by the system to infer such structural knowledge, without relying on the expertise of a skilled user. This will allow researchers to shift their focus back from the modelling task to the actual utilization of the model. Such a research objective requires the identification technique to employ tools that are not targeted towards nuanced modelling tasks, but remain applicable for a very broad range of systems. Hence, we seek to develop a new framework for system identification that uses generic tools.

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Khandelwal D, Toth R, Van den Hof PMJ. Data-driven modelling of LTI systems using symbolic regression. 2017. Abstract van 36th Benelux Meeting on Systems and Control, 28-30 March 2017, Spa, Belgium, Spa, België.