A Bayesian approach for estimation of linear-regression LPV models

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

13 Citations (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.
Original languageEnglish
Title of host publicationProceedings of the 53rd IEEE Conference on Decision and Control, (CDC), 15-17 december 2014, Los Angeles, California, United States
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-4799-7746-8
Publication statusPublished - 2014
Event53rd IEEE Conference on Decision and Control, CDC 2014 - "J.W. Marriott Hotel", Los Angeles, United States
Duration: 15 Dec 201417 Dec 2014
Conference number: 53


Conference53rd IEEE Conference on Decision and Control, CDC 2014
Abbreviated titleCDC
Country/TerritoryUnited States
CityLos Angeles
Internet address


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