Non-parametric identification of linear parameter-varying spatially-interconnected systems using an LS-SVM approach

Q. Liu, J. Mohammadpour, R. Toth, N. Meskin

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

1 Citaat (Scopus)

Uittreksel

This paper considers a general approach for the identification of partial differential equation-governed spatially-distributed systems. Spatial discretization virtually divides a system into spatially-interconnected subsystems, which allows to define the identification problem at the subsystem level. Here we focus on such a distributed identification of spatially-interconnected systems with temporal/spatial varying properties, whose dynamics can be captured by temporal/spatial linear parameter-varying (LPV) models. Inaccurate selection of the functional dependencies of the model parameters on scheduling variables may lead to bias in the identified models. Hence, we propose a non-parametric identification approach via a least-squares support vector machine (LS-SVM) - `non-parametric' estimation is in the sense that the model dependence on the scheduling variables is not explicitly parametrized. The performance of the proposed approach is evaluated on an Euler-Bernoulli beam with varying thickness.
Originele taal-2Engels
TitelProceedings of the American Control Conference (ACC), 6-8 July 2016, Boston, Massachusetts
UitgeverijAmerican Automatic Control Council (AACC)
Pagina's4592-4597
ISBN van geprinte versie978-1-4673-8682-1
DOI's
StatusGepubliceerd - 2016
Evenement2016 American Control Conference (ACC 2016), July 6-8, 2016, Boston, MA, USA - Boston Marriott Copley Place, Boston, MA, Verenigde Staten van Amerika
Duur: 6 jul 20168 jul 2016
http://acc2016.a2c2.org/

Congres

Congres2016 American Control Conference (ACC 2016), July 6-8, 2016, Boston, MA, USA
Verkorte titelACC 2016
LandVerenigde Staten van Amerika
StadBoston, MA
Periode6/07/168/07/16
Internet adres

Vingerafdruk

Support vector machines
Large scale systems
Identification (control systems)
Scheduling
Partial differential equations

Citeer dit

Liu, Q., Mohammadpour, J., Toth, R., & Meskin, N. (2016). Non-parametric identification of linear parameter-varying spatially-interconnected systems using an LS-SVM approach. In Proceedings of the American Control Conference (ACC), 6-8 July 2016, Boston, Massachusetts (blz. 4592-4597). American Automatic Control Council (AACC). https://doi.org/10.1109/ACC.2016.7526076
Liu, Q. ; Mohammadpour, J. ; Toth, R. ; Meskin, N. / Non-parametric identification of linear parameter-varying spatially-interconnected systems using an LS-SVM approach. Proceedings of the American Control Conference (ACC), 6-8 July 2016, Boston, Massachusetts. American Automatic Control Council (AACC), 2016. blz. 4592-4597
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abstract = "This paper considers a general approach for the identification of partial differential equation-governed spatially-distributed systems. Spatial discretization virtually divides a system into spatially-interconnected subsystems, which allows to define the identification problem at the subsystem level. Here we focus on such a distributed identification of spatially-interconnected systems with temporal/spatial varying properties, whose dynamics can be captured by temporal/spatial linear parameter-varying (LPV) models. Inaccurate selection of the functional dependencies of the model parameters on scheduling variables may lead to bias in the identified models. Hence, we propose a non-parametric identification approach via a least-squares support vector machine (LS-SVM) - `non-parametric' estimation is in the sense that the model dependence on the scheduling variables is not explicitly parametrized. The performance of the proposed approach is evaluated on an Euler-Bernoulli beam with varying thickness.",
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Liu, Q, Mohammadpour, J, Toth, R & Meskin, N 2016, Non-parametric identification of linear parameter-varying spatially-interconnected systems using an LS-SVM approach. in Proceedings of the American Control Conference (ACC), 6-8 July 2016, Boston, Massachusetts. American Automatic Control Council (AACC), blz. 4592-4597, Boston, MA, Verenigde Staten van Amerika, 6/07/16. https://doi.org/10.1109/ACC.2016.7526076

Non-parametric identification of linear parameter-varying spatially-interconnected systems using an LS-SVM approach. / Liu, Q.; Mohammadpour, J.; Toth, R.; Meskin, N.

Proceedings of the American Control Conference (ACC), 6-8 July 2016, Boston, Massachusetts. American Automatic Control Council (AACC), 2016. blz. 4592-4597.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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AB - This paper considers a general approach for the identification of partial differential equation-governed spatially-distributed systems. Spatial discretization virtually divides a system into spatially-interconnected subsystems, which allows to define the identification problem at the subsystem level. Here we focus on such a distributed identification of spatially-interconnected systems with temporal/spatial varying properties, whose dynamics can be captured by temporal/spatial linear parameter-varying (LPV) models. Inaccurate selection of the functional dependencies of the model parameters on scheduling variables may lead to bias in the identified models. Hence, we propose a non-parametric identification approach via a least-squares support vector machine (LS-SVM) - `non-parametric' estimation is in the sense that the model dependence on the scheduling variables is not explicitly parametrized. The performance of the proposed approach is evaluated on an Euler-Bernoulli beam with varying thickness.

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Liu Q, Mohammadpour J, Toth R, Meskin N. Non-parametric identification of linear parameter-varying spatially-interconnected systems using an LS-SVM approach. In Proceedings of the American Control Conference (ACC), 6-8 July 2016, Boston, Massachusetts. American Automatic Control Council (AACC). 2016. blz. 4592-4597 https://doi.org/10.1109/ACC.2016.7526076