Identification of noisy input-output system using bias-compensated least-squares method

In this paper, a new bias-compensated least-squares (BCLS) based algorithm is proposed for identification of noisy input-output system. It is well known that BCLS method is based on compensation of asymptotic bias on the least-squares (LS) estimates by making use of noise variances estimates. The main feature of the proposed algorithm is to introduce a generalized least-squares type estimator in order to obtain the good estimates of noise variances. The results of a simulated example indicate that the proposed algorithm provides good estimates.

[1]  Torsten Söderström,et al.  Identification of stochastic linear systems in presence of input noise , 1981, Autom..

[2]  Wei Xing Zheng.  Fast adaptive IIR filtering with noisy input and output data , 2001, Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467).

[3]  K. Wada,et al.  On-line modified least-squares parameter estimation of linear discrete dynamic systems , 1977 .

[4]  Chun-Bo Feng,et al.  Unbiased parameter estimation of linear systems in the presence of input and output noise , 1989 .

[5]  Wei Xing Zheng,et al.  A bias correction method for identification of linear dynamic errors-in-variables models , 2002, IEEE Trans. Autom. Control..

[6]  Kazushi Nakano,et al.  Identification of Pulse Transfer Function in the Presence of Input and Output Noise , 1992 .

[7]  K. Wada,et al.  On bias compensated least squares method for noisy input-output system identification , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[8]  K. Fernando,et al.  Identification of linear systems with input and output noise: the Koopmans-Levin method , 1985 .

[9]  W. Zheng.  On least‐squares identification of stochastic linear systems with noisy input–output data , 1999 .

[10]  Wei Xing Zheng,et al.  Unbiased identification of stochastic linear systems from noisy input and output measurements , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).