Recursive extended least squares identification method based on auxiliary models
基于辅助模型的递推增广最小二乘辨识方法

For output error moving average systems with colored noises (OEMA model), this paper combines the auxiliary model and the recursive extended least squares algorithm to present the auxiliary model based recursive extended least squares (AMRELS) algorithm. In this approach, we replace the unknown true outputs in the information vector with the outputs of the auxiliary model, and replace the immeasurable noise terms in the information vector with the estimated residuals. To demonstrate the advantage of the proposed algorithm, this paper gives the recursive extended least squares algorithm through model transformation. The analysis and simulation results indicate that the AMRELS algorithm is simple in principle, with less computational burden, capable of generating more accurate parameter estimates and can be implemented on-line.