Input-output Data Filter based Recursive Bayesian Algorithm with Covariance Resetting for Identifica

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  To identify systems with Non-uniformly sampled input data,a filter based recursive identification algorithm with covariance resetting is proposed.Using estimated noise transfer function as a dynamic filter,the algorithm transforms the systems with colored noise into systems with white noise.To obtain improved estimates,the estimated noise variance is employed in the algorithm.Meanwhile a novel covariance resetting method is also integrated in the algorithm to improve the convergence rate.A simulation example validates the proposed algorithm.
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