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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