relation: http://eprints.imtlucca.it/2460/
title: LPV model order selection in an LS-SVM setting
creator: Piga, Dario
creator: Tóth, Roland
subject: QA75 Electronic computers. Computer science
description: In parametric identification of Linear Parameter-Varying (LPV) systems, the scheduling dependencies of the model coefficients are commonly parameterized in terms of linear combinations of a-priori selected basis functions. Such functions need to be adequately chosen, e.g., on the basis of some first-principles or expert's knowledge of the system, in order to capture the unknown dependencies of the model coefficient functions on the scheduling variable and, at the same time, to achieve a low-variance of the model estimate by limiting the number of parameters to be identified. This problem together with the well-known model order selection problem (in terms of number of input lags, output lags and input delay of the model structure) in system identification can be interpreted as a trade-off between bias and variance of the resulting model estimate. The problem of basis function selection can be avoided by using a non-parametric estimator of the coefficient functions in terms of a recently proposed Least-Square Support-Vector-Machine (LS-SVM) approach. However, the selection of the model order still appears to be an open problem in the identification of LPV systems via the LS-SVM method. In this paper, we propose a novel reformulation of the LPV LS-SVM approach, which, besides of the non-parametric estimation of the coefficient functions, achieves data-driven model order selection via convex optimization. The properties of the introduced approach are illustrated via a simulation example.
publisher: IEEE
date: 2013-12
type: Book Section
type: PeerReviewed
format: application/pdf
language: en
identifier: http://eprints.imtlucca.it/2460/1/CDC2013Preprint_Piga.pdf
identifier:   Piga, Dario and Tóth, Roland  LPV model order selection in an LS-SVM setting.   In:  Proceedings of the 52nd Annual Conference on Decision and Control (CDC), 2013.   IEEE, pp. 4128-4133.  ISBN 978-1-4673-5714-2       (2013)  
relation: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6760522&isnumber=6759837
relation: 10.1109/CDC.2013.6760522