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

2018年第03期

关键词:
Parameter estimation discrete time systems Gaussian noise Bayesian algorithm covariance resetting

Keywords
Parameter estimation;discrete time systems;Gaussian noise;Bayesian algorithm;covariance resetting
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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.


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