Shao-Xue Jing, Tian-Hong Pan and Zheng-Ming Li. Recursive Bayesian Algorithm for Identification of Systems with Non-uniformly Sampled Input Data. International Journal of Automation and Computing, vol. 15, no. 3, pp. 335-344, 2018. DOI: 10.1007/s11633-017-1073-z
Citation: Shao-Xue Jing, Tian-Hong Pan and Zheng-Ming Li. Recursive Bayesian Algorithm for Identification of Systems with Non-uniformly Sampled Input Data. International Journal of Automation and Computing, vol. 15, no. 3, pp. 335-344, 2018. DOI: 10.1007/s11633-017-1073-z

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 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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