Recursive Bayesian Algorithm for Identification of Systems with Non-uniformly Sampled Input Data

To identify systems with non-uniformly sampled input data, a recursive Bayesian identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the system with colored noise is transformed into the system with white noise. In order to impr...

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Bibliographic Details
Published in:Machine intelligence research (Print) Vol. 15; no. 3; pp. 335 - 344
Main Authors: Jing, Shao-Xue, Pan, Tian-Hong, Li, Zheng-Ming
Format: Journal Article
Language:English
Published: Beijing Springer Nature B.V 01.06.2018
Subjects:
Algorithms
Automation
Bayesian analysis
Covariance
Estimates
Identification
Kalman filters
Parameter estimation
Transfer functions
White noise
ISSN:2153-182X, 2153-1838
Online Access:Get full text
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Summary:To identify systems with non-uniformly sampled input data, a recursive Bayesian identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the system with colored noise is transformed into the system with white noise. In order to improve estimates, the estimated noise variance is employed as a weighting factor in the algorithm. Meanwhile, a modified covariance resetting method is also integrated in the proposed algorithm to increase the convergence rate. A numerical example and an industrial example validate the proposed algorithm.
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ISSN:2153-182X
2153-1838
DOI:10.1007/s11633-017-1073-z