Recursive Bayesian algorithm with covariance resetting for identification of OEAR models with non-uniformly sampled input data

Abstract

To identify the OEAR model with non-uniformly sampled input data, a recursive Bayesian identification algorithm with covariance resetting is proposed in this paper. Comparing with the conventional recursive least squares algorithm based on auxiliary model, the presented algorithm considers the variance of the colored noise and can estimate the parameter with high accuracy. Furthermore, the algorithm integrates the prior probability density function of the parameters and the prior probability density function of the process data together, and achieves better performance than the maximum likelihood algorithm. To improve the convergence rate, a new covariance resetting method is also integrated in the algorithm. A simulation example demonstrates the performance of the proposed algorithm.