Iterative parameter identification for pseudo-linear systems with ARMA noise using the filtering technique

作者：Ding, Feng^*; Xu, Ling; Alsaadi, Fuad E.; Hayat, Tasawar

来源：IET Control Theory and Applications, 2018, 12(7): 892-899.

DOI：10.1049/iet-cta.2017.0821

• linear systems
• autoregressive moving average processes
• filtering theory
• parameter estimation
• iterative methods
• gradient methods
• least squares approximations
• iterative parameter identification
• ARMA noise
• filtering technique
• pseudolinear autoregressive moving average system
• linear-in-parameter autoregressive output-error ARMA systems
• filtering-based gradient iterative algorithm
• F-GI algorithm
• filtering-based least squares iterative algorithm
• LSI algorithms

This study considers the parameter identification problem of a pseudo-linear autoregressive moving average system (i.e. linear-in-parameter autoregressive output-error ARMA systems), whose disturbance is an ARMA process. By means of the filtering technique, a filtering-based gradient iterative (F-GI) algorithm and a filtering-based least squares iterative (LSI) algorithms are presented for enhancing the estimation accuracy. Furthermore, a filtering-based decomposition LSI algorithm is derived for improving the computational efficiency. The key is to use the hierarchical identification principle, to apply the data filtering technique for identification, and to replace the unknown terms in the information vectors with their estimates. Compared with the F-GI algorithm, the filtering-based LSI algorithm and the filtering-based decomposition LSI algorithm have faster convergence rates. The simulation results indicate that the proposed algorithms are effective.

• 出版日期2018-5
• 单位青岛科技大学; 江南大学