Recursive Bayesian Algorithm with Covariance Resetting for Identification of Box–Jenkins Systems with Non-uniformly Sampled Input Data - Jing, Shaoxue; Pan, Tianhong; Li, Zhengming (Circuits, systems, and signal processing, 2016)
To identify the Box–Jenkins systems with non-uniformly sampled input data, a recursive Bayesian algorithm with covariance resetting was proposed in this paper. Considering the prior probability density functions of parameters and the observed input–output data, the parameters were estimated by maximizing the posterior probability distribution function. During the estimation, the variance of the noise was taken as a weighting factor, and the proposed algorithm was formulated as a weighted least squares. As a result, the accuracy of the estimates increased. Meanwhile, a modified covariance resetting strategy was integrated into the algorithm to improve the convergence rate, and the convergence of the algorithm was also analyzed. A simulation example was applied to validate the proposed algorithm.