Variational Bayesian approach for ARX systems with missing observations and varying time-delays

This paper develops a variational Bayesian approach for identifying ARX models with missing observations and varying time-delays. The outputs of the ARX models are subject to both slow sampling rates and communication delays. The unknown missing observations which are used in the variational Bayesia...

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Bibliographic Details
Published inAutomatica (Oxford) Vol. 94; pp. 194 - 204
Main Authors Chen, Jing, Huang, Biao, Ding, Feng, Gu, Ya
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.08.2018
Subjects
Kalman filtering method
Missing observations
Parameter estimation
Variational Bayesian approach
Varying time-delay
Parameter estimation
Varying time-delay
Variational Bayesian approach
Kalman filtering method
Missing observations
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Summary:This paper develops a variational Bayesian approach for identifying ARX models with missing observations and varying time-delays. The outputs of the ARX models are subject to both slow sampling rates and communication delays. The unknown missing observations which are used in the variational Bayesian approach can be estimated by a modified Kalman filter, and based on the estimated missing observations and available data, the unknown parameters and the varying time-delays can be estimated by using the variational Bayesian approach. The simulation results demonstrate that the variational Bayesian method is effective.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2018.04.003