%0 Journal Article
%T Recursive extended least squares identification method based on auxiliary models<br>基于辅助模型的递推增广最小二乘辨识方法
%A WANG Dong-qing
%A <br>王冬青
%J 控制理论与应用
%D 2009
%I 
%X For output error moving average systems with colored noises (OEMA model), this paper combines the auxiliary model and the recursive extended least squares algorithm to present the auxiliary model based recursive extended least squares (AMRELS) algorithm. In this approach, we replace the unknown true outputs in the information vector with the outputs of the auxiliary model, and replace the immeasurable noise terms in the information vector with the estimated residuals. To demonstrate the advantage of the proposed algorithm, this paper gives the recursive extended least squares algorithm through model transformation. The analysis and simulation results indicate that the AMRELS algorithm is simple in principle, with less computational burden, capable of generating more accurate parameter estimates and can be implemented on-line.
%K recursive identification
%K parameter estimation
%K least squares
%K auxiliary models
%K output error moving average models (OEMA)<br>递推辨识
%K 参数估计
%K 最小二乘
%K 辅助模型
%K 输出误差滑动甲均模型
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=212D183E1BD69DE740032B7A303EB847&yid=DE12191FBD62783C&vid=96C778EE049EE47D&iid=CA4FD0336C81A37A&sid=987EDA49D8A7A635&eid=014B591DF029732F&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=2&reference_num=19