Parameter estimation for ARMAX systems using bias compensation methods

Authors:
Yong Zhang;Gui-Mei Cui;Xin Liu
Affiliations:
School of Information Engineering, Inner Mongolia University and Technology, Baotou, China;School of Information Engineering, Inner Mongolia University and Technology, Baotou, China;School of Information Engineering, Inner Mongolia University and Technology, Baotou, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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System identification (2nd ed.): theory for the user

System identification (2nd ed.): theory for the user
Parameter estimation of stochastic linear systems with noisy input

International Journal of Systems Science

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Abstract

For ARMAX systems, this paper derives a bias compensation recursive least squares (BCRLS) identification algorithm by means of the prefilter ieda and the bias compensation principle. The proposed algorithm realizes the recursive computation of the bias compensation methods and can be on-line implemented. The BCRLS algorithm can give the unbiased estimation of the system model parameters in the presence of colored noises, irrespective of the noise model. Finally, the advantages of the proposed BCRLS algorithm over the non-recursive bias compensation least squares (BCLS) algorithm are shown by simulation test.