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

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Abstract

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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Authors and Affiliations

  1. School of Electrical Information and Engineering, Jiangsu University, Zhenjiang, 212013, China

    Shao-Xue Jing, Tian-Hong Pan & Zheng-Ming Li

  2. Department of Electrical Engineering, Huaian College of Information and Technology, Huaian, 223003, China

    Shao-Xue Jing

Authors
  1. Shao-Xue Jing
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  2. Tian-Hong Pan
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Corresponding author

Correspondence to Tian-Hong Pan.

Additional information

This work was supported by National Natural Science Foundation of China (Nos. 61273142 and 51477070), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Foundation for Six Talents by Jiangsu Province and Graduate Scientific Innovation Projects of Jiangsu University (No.KYXX 0003)

Recommended by Associate Editor Ding-Li Yu

Shao-Xue Jing received the B. Sc. degree from Anhui Agriculture University and the M. Sc. degree from Gansu University of Technology, China in 1997 and 2000, respectively. And he received the Ph.D. degree in control theory and control engineering from Shanghai Jiao Tong University, China in 2007. Now he has been a professor in School of Electrical and Information Engineering, Jiangsu University, China.

His research interests include multiple model approach and its application, machine learning, virtual metrology, predictive control and run-to-run control theory and practice, system identification.

Tian-Hong Pan received the B. Sc. degree from Anhui Agriculture University, the M. Sc. degree from Gansu University of Technology, China in 1997 and 2000, respectively. And he received the Ph.D. degree in control theory and control engineering from Shanghai Jiao Tong University, China in 2007. Now he has been a professor in School of Electrical and Information Engineering, Jiangsu University, China.

His research interests include multiple model approach and its application, machine learning, virtual metrology, predictive control and run-to-run control theory and practice, system identification.

Zheng-Ming Li received the B. Sc. degree from the Zhenjiang Institute of Agricultural Machinery, China in 1982, and the M. Sc. degree in control theory and control engineering from the Department of Automation, Xi-an Jiao Tong University, China in 1987. He has been a professor in the School of Electrical Information and Engineering, Jiangsu University, China.

His research interests include modeling of complex processes and wireless sensor network.

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Jing, SX., Pan, TH. & Li, ZM. Recursive Bayesian Algorithm for Identification of Systems with Non-uniformly Sampled Input Data. Int. J. Autom. Comput. 15, 335–344 (2018). https://doi.org/10.1007/s11633-017-1073-z

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  • DOI: https://doi.org/10.1007/s11633-017-1073-z

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