Least squares based iterative algorithm for pseudo-linear autoregressive moving average systems using the data filtering technique

Abstract This paper concentrates on the identification problems of pseudo-linear autoregressive moving average systems. A least squares based iterative (LSI) algorithm is proposed using the data filtering technique and an LSI algorithm is developed for comparisons. The basic idea is to use a linear filter to filter the input–output data, to decompose a pseudo-linear autoregressive moving average system into a system model and a noise model. Finally, two examples are given to confirm the effectiveness of the proposed algorithms.

[1]  Er-Wei Bai,et al.  Convergence of the iterative Hammerstein system identification algorithm , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[2]  Feng Ding,et al.  State filtering and parameter estimation for linear systems with d-step state-delay , 2014, IET Signal Process..

[3]  Jing Lu,et al.  Least squares based iterative identification for a class of multirate systems , 2010, Autom..

[4]  Feng Ding,et al.  Recursive least squares parameter identification algorithms for systems with colored noise using the filtering technique and the auxilary model , 2015, Digit. Signal Process..

[5]  Huazhen Fang,et al.  Kalman filter-based identification for systems with randomly missing measurements in a network environment , 2010, Int. J. Control.

[6]  Tao Tang,et al.  Recursive least squares estimation algorithm applied to a class of linear-in-parameters output error moving average systems , 2014, Appl. Math. Lett..

[7]  Tao Tang,et al.  Several gradient-based iterative estimation algorithms for a class of nonlinear systems using the filtering technique , 2014 .

[8]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[9]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[10]  D. Wang Brief paper: Lleast squares-based recursive and iterative estimation for output error moving average systems using data filtering , 2011 .

[11]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[12]  Erfu Yang,et al.  A filtering based recursive least squares estimation algorithm for pseudo-linear auto-regressive systems , 2014, J. Frankl. Inst..

[13]  Feng Ding,et al.  States based iterative parameter estimation for a state space model with multi-state delays using decomposition , 2015, Signal Process..

[14]  Kang Li,et al.  Convergence of the iterative algorithm for a general Hammerstein system identification , 2010, Autom..