Recursive Bayesian Algorithm with Covariance Resetting for Identification of Box–Jenkins Systems with Non-uniformly Sampled Input Data

To identify the Box–Jenkins systems with non-uniformly sampled input data, a recursive Bayesian algorithm with covariance resetting was proposed in this paper. Considering the prior probability density functions of parameters and the observed input–output data, the parameters were estimated by maxim...

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Bibliographic Details
Published in:Circuits, systems, and signal processing Vol. 35; no. 3; pp. 919 - 932
Main Authors: Jing, Shaoxue, Pan, Tianhong, Li, Zhengming
Format: Journal Article
Language:English
Published: New York Springer US 01.03.2016
Springer Nature B.V
Subjects:
Algorithms
Bayesian analysis
Circuits
Circuits and Systems
Convergence
Covariance
Electrical Engineering
Electronics and Microelectronics
Engineering
Instrumentation
Least squares method
Noise
Recursive
Signal,Image and Speech Processing
Strategy
Non-uniformly sampled data system
Box–Jenkins system
Covariance resetting
Recursive Bayesian algorithm
ISSN:0278-081X, 1531-5878
Online Access:Get full text
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Summary:To identify the Box–Jenkins systems with non-uniformly sampled input data, a recursive Bayesian algorithm with covariance resetting was proposed in this paper. Considering the prior probability density functions of parameters and the observed input–output data, the parameters were estimated by maximizing the posterior probability distribution function. During the estimation, the variance of the noise was taken as a weighting factor, and the proposed algorithm was formulated as a weighted least squares. As a result, the accuracy of the estimates increased. Meanwhile, a modified covariance resetting strategy was integrated into the algorithm to improve the convergence rate, and the convergence of the algorithm was also analyzed. A simulation example was applied to validate the proposed algorithm.
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ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-015-0094-5