| 引用本文: | 王冬青.基于辅助模型的递推增广最小二乘辨识方法[J].控制理论与应用,2009,26(1):51~56.[点击复制] |
| WANG Dong-qing.Recursive extended least squares identification method based on auxiliary models[J].Control Theory and Technology,2009,26(1):51~56.[点击复制] |
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| 基于辅助模型的递推增广最小二乘辨识方法 |
| Recursive extended least squares identification method based on auxiliary models |
| 摘要点击 2298 全文点击 2521 投稿时间：2007-09-14 修订日期：2008-03-20 |
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| DOI编号 10.7641/j.issn.1000-8152.2009.1.009 |
| 2009,26(1):51-56 |
| 中文关键词 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 |
| 英文关键词 recursive identification parameter estimation least squares auxiliary models output error moving average models (OEMA) |
| 基金项目 山东省高等学校优秀青年教师国内访问学者项目; 国家自然科学基金资助项目(60673101). |
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| 中文摘要 |
| 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. |
| 英文摘要 |
| 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. |