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AuthorAbbasi, F.
AuthorMohammadpour, J.
AuthorToth, R.
AuthorMeskin, Nader
Available date2022-04-14T08:45:44Z
Publication Date2014
Publication Name2014 European Control Conference, ECC 2014
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ECC.2014.6862581
URIhttp://hdl.handle.net/10576/29816
AbstractIn this paper, we present a method that utilizes support vector machines (SVM) to identify linear parameter-varying (LPV) auto-regressive exogenous input (ARX) models corrupted by not only noise, but also uncertainties in the LPV scheduling variables. The proposed method employs SVM and takes advantage of the so-called 'kernel trick' to allow for the identification of the LPV-ARX model structure solely based on the input-output data. The objective function, as defined in this paper, allows to consider uncertainties related to the LPV scheduling parameters, and hence results in a new formulation that provides a more accurate estimation of the LPV model in the presence of scheduling uncertainties. We further demonstrate the viability of the proposed LPV identification method through numerical examples, where we show that higher best fit rate (BFR) can be achieved under realistic noise conditions using the proposed method compared to the method initially proposed in [6]. 2014 EUCA.
SponsorQatar National Research Fund
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectNumerical methods
Scheduling
Support vector machines
Accurate estimation
Auto-regressive exogenous inputs
Identification method
Input-output data
Linear parameter varying
Objective functions
Scheduling parameters
Scheduling variable
Parameter estimation
TitleA support vector machine-based method for LPV-ARX identification with noisy scheduling parameters
TypeConference Paper
Pagination370-375


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