文档介绍：第 26 卷第 1 期控制理论与应用 Vol. 26 No. 1
2009 年 1 月 Control Theory & Applications Jan. 2009
文文文章章章编编编号号号: 1000−8152(2009)01−0051−06
基基基于于于辅辅辅助助助模模模型型型的的的递递递推推推增增增广广广最最最小小小二二二乘乘乘辨辨辨识识识方方方法法法
王冬青
(青岛大学自动化工程学院, 山东青岛 266071)
摘要: 针对有色噪声干扰的输出误差滑动平均系统, 将辅助模型与递推增广最小二乘算法相结合: 用辅助模型的
输出代替辨识模型信息向量中的未知真实输出项, 用估计残差代替信息向量中的不可测噪声项, 从而提出了基于辅
助模型的递推增广最小二乘辨识方法. 为了展示所提方法的特点, 文中还给出了经过模型变换的递推增广最小二
乘算法. 理论分析和仿真研究表明, 提出的方法原理简单、计算量小, 可以给出高精度参数估计, 且能够用于在线辨
识.
关键词: 递推辨识; 参数估计; 最小二乘; 辅助模型; 输出误差滑动平均模型
中图分类号: TP273 文献标识码: A
Recursive extended least squares identification
method based on auxiliary models
WANG Dong-qing
(College of Automation Engineering, Qingdao University, Qingdao Shandong 266071, China)
Abstract: For output error moving average systems with colored noises (OEMA model), this bines 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 AMRE