Uittreksel
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.
| Taal | Engels |
|---|---|
| Pagina's | 145 |
| Status | Gepubliceerd - 2017 |
| Evenement | 36th Benelux Meeting on Systems and Control, 28-30 March 2017, Spa, Belgium - Sol-Cress, Spa, België Duur: 28 mrt 2017 → 30 mrt 2017 http://www.beneluxmeeting.nl/2017/ |
Congres
| Congres | 36th Benelux Meeting on Systems and Control, 28-30 March 2017, Spa, Belgium |
|---|---|
| Land | België |
| Stad | Spa |
| Periode | 28/03/17 → 30/03/17 |
| Internet adres |
Vingerafdruk
Citeer dit
}
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 congres › Abstract › Academic
TY - CONF
T1 - Data-driven modelling of LTI systems using symbolic regression
AU - Khandelwal,D.
AU - Toth,R.
AU - Van den Hof,P.M.J.
PY - 2017
Y1 - 2017
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.
M3 - Abstract
SP - 145
ER -