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

doi:10.1007/s11633-017-1073-z

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	Recursive Bayesian Algorithm for Identification of Systems with Non-uniformly Sampled Input Data
	Shao-Xue Jing 1,2; Tian-Hong Pan 2; Zheng-Ming Li 2
刊名	International Journal of Automation and Computing
	2018
卷号	15 期号:3 页码:335-344
关键词	Parameter estimation discrete time systems Gaussian noise Bayesian algorithm covariance resetting.
ISSN号	1476-8186
DOI	10.1007/s11633-017-1073-z
英文摘要	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.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42414]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	1.Department of Electrical Engineering, Huaian College of Information and Technology, Huaian 223003, China 2.School of Electrical Information and Engineering, Jiangsu University, Zhenjiang 212013, China
推荐引用方式 GB/T 7714	Shao-Xue Jing,Tian-Hong Pan,Zheng-Ming Li. Recursive Bayesian Algorithm for Identification of Systems with Non-uniformly Sampled Input Data[J]. International Journal of Automation and Computing,2018,15(3):335-344.
APA	Shao-Xue Jing,Tian-Hong Pan,&Zheng-Ming Li.(2018).Recursive Bayesian Algorithm for Identification of Systems with Non-uniformly Sampled Input Data.International Journal of Automation and Computing,15(3),335-344.
MLA	Shao-Xue Jing,et al."Recursive Bayesian Algorithm for Identification of Systems with Non-uniformly Sampled Input Data".International Journal of Automation and Computing 15.3(2018):335-344.