A Bayesian approach for estimation of linear-regression LPV models

A. Golabi, N. Meskin, R. Toth, J. Mohammadpour

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

13 Citaten (Scopus)
1 Downloads (Pure)


In this paper, a Bayesian framework for iden- tification of linear parameter-varying (LPV) models with fi- nite impulse response (FIR) dynamic structure is introduced, in which the dependency structure of LPV system on the scheduling variables is identified based on a Gaussian Process (GP) formulation. Using this approach, a GP is employed to describe the distribution of the coefficient functions, that are dependent on the scheduling variables, in LPV linear- regression models. First, a prior distribution over the nonlinear functions representing the unknown coefficient dependencies of the model to be estimated is defined; then, a posterior distribution of these functions is obtained given measured data. The mean value of the posterior distribution is used to provide a model estimate. The approach is formulated with both static and dynamic dependency of the coefficient functions on the scheduling variables. The properties and performance of the proposed method are evaluated using illustrative examples.
Originele taal-2Engels
TitelProceedings of the 53rd IEEE Conference on Decision and Control, (CDC), 15-17 december 2014, Los Angeles, California, United States
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
ISBN van geprinte versie978-1-4799-7746-8
StatusGepubliceerd - 2014
Evenement53rd IEEE Conference on Decision and Control, CDC 2014 - "J.W. Marriott Hotel", Los Angeles, Verenigde Staten van Amerika
Duur: 15 dec. 201417 dec. 2014
Congresnummer: 53


Congres53rd IEEE Conference on Decision and Control, CDC 2014
Verkorte titelCDC
Land/RegioVerenigde Staten van Amerika
StadLos Angeles
Ander53rd IEEE Conference on Decision and Control
Internet adres


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