Abstract
Accurate parametric identification of Linear Parameter-Varying (LPV) systems requires an optimal prior selection of a set of functional dependencies for the parametrization of the model coefficients. Inaccurate selection leads to structural bias while over-parametrization results in a variance increase of the estimates. This corresponds to the classical bias-variance trade-off, but with a significantly larger degree of freedom and sensitivity in the LPV case. Hence, it is attractive to estimate the underlying model structure of LPV systems based on measured data, i.e., to learn the underlying dependencies of the model coefficients together with model orders etc. In this paper a Least-Squares Support Vector Machine (LS-SVM) approach is introduced which is capable of reconstructing the dependency structure for linear regression based LPV models even in case of rational dynamic dependency. The properties of the approach are analyzed in the prediction error setting and its performance is evaluated on representative examples.
Original language | English |
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Title of host publication | Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), 12-15 December 2012, Orlando, Florida |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 3192-3197 |
ISBN (Print) | 978-1-61284-800-6 |
DOIs | |
Publication status | Published - 2011 |
Event | 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011) - Hilton Orlando Bonnet Creek, Orlando, United States Duration: 12 Dec 2011 → 15 Dec 2011 Conference number: 50 http://www.ieeecss.org/CAB/conferences/cdcecc2011/ |
Conference
Conference | 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011) |
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Abbreviated title | CDC-ECC 2011 |
Country/Territory | United States |
City | Orlando |
Period | 12/12/11 → 15/12/11 |
Other | 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 |
Internet address |