Show simple item record

AuthorAbbasi, F.
AuthorMohammadpour, J.
AuthorToth, R.
AuthorMeskin, Nader
Available date2022-04-14T08:45:43Z
Publication Date2015
Publication NameProceedings of the IEEE Conference on Decision and Control
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/CDC.2015.7402326
URIhttp://hdl.handle.net/10576/29805
AbstractThis paper presents a Gaussian Process (GP) based Bayesian method that takes into account the effect of additive noise on the scheduling variables for identification of linear parameter-varying (LPV) models in input-output form. The proposed method approximates the noise-free coefficient functions by a local linear expansion on the observed scheduling variables. Therefore, additive noise on the scheduling variables is reconstructed as a corrective term added to the output noise that is proportional to the squared gradient obtained from the posterior of the Gaussian Process. An iterative procedure is given so that the obtained solution converges to the best estimation of the coefficient functions according to the given measure of fitness. Moreover, the expectation and covariance functions estimated by GP are modified for the noisy scheduling variable case to include the noise contribution on the estimated expectation and covariance functions. The model training procedure identifies noise level in the measurements including outputs and scheduling variables by estimating the noise variances, as well as other defined hyperparameters. Finally, the performance of the proposed method is compared to the standard GP approach through a numerical example. 2015 IEEE.
SponsorQatar National Research Fund
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBayesian networks
Gaussian distribution
Gaussian noise (electronic)
Iterative methods
Numerical methods
Scheduling
Bayesian approaches
Coefficient functions
Covariance function
Gaussian Processes
Linear parameter varying models
Model identification
Noise contributions
Scheduling variable
Additive noise
TitleA Bayesian approach for model identification of LPV systems with uncertain scheduling variables
TypeConference Paper
Pagination789-794
Volume Number54rd IEEE Conference on Decision and Control,CDC 2015
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record