@inproceedings{61e1f0148bd648d796cc86e25a529c0c,
title = "Bias compensation based recursive least squares identification for equation error models with colored noises",
abstract = "It is well known that the least squares estimation of ARMAX models is biased. In this paper, by combining the principle of bias compensation and hierarchical identification, a new identification is established for this equation error model with moving average noises. The proposed estimate of the system parameter is given by the least squares estimate modified by a correction term. A numerical example is employed to show the advantage of the proposed estimation algorithm.",
keywords = "Bias compensation, Covariance, Least squares estimation",
author = "Wu, \{Ai Guo\} and Fan Yang and Qian, \{Yang Yang\}",
note = "Publisher Copyright: {\textcopyright} 2014 TCCT, CAA.; Proceedings of the 33rd Chinese Control Conference, CCC 2014 ; Conference date: 28-07-2014 Through 30-07-2014",
year = "2014",
month = sep,
day = "11",
doi = "10.1109/ChiCC.2014.6896104",
language = "英语",
series = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",
publisher = "IEEE Computer Society",
pages = "6715--6720",
editor = "Shengyuan Xu and Qianchuan Zhao",
booktitle = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",
address = "美国",
}