| 引用本文: | 杨慧中, 张勇.Box-Jenkins模型偏差补偿方法与其他辨识方法的比较[J].控制理论与应用,2007,24(2):215~222.[点击复制] |
| YANG Hui-zhong,ZHANG Yong.Comparisons of bias compensation methods and other identification approaches for Box-Jenkins models[J].Control Theory and Technology,2007,24(2):215~222.[点击复制] |
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| Box-Jenkins模型偏差补偿方法与其他辨识方法的比较 |
| Comparisons of bias compensation methods and other identification approaches for Box-Jenkins models |
| 摘要点击 1466 全文点击 828 投稿时间：2005-08-25 修订日期：2006-06-05 |
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| DOI编号 |
| 2007,24(2):215-222 |
| 中文关键词 Box-Jenkins模型 最小二乘 参数估计 辨识 偏差补偿 |
| 英文关键词 Box-Jenkins models least squares parameter estimation identification bias compensation |
| 基金项目 国家自然科学基金资助项目(60574051,60674092);江苏省高技术研究(工业)项目(BG200610). |
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| 中文摘要 |
| 对于存在相关噪声干扰的Box-Jenkins系统,本文借助于偏差补偿原理, 推导了一个偏差补偿最小二乘(BCLS)辨识方法;理论分析说明BCLS方法能够给出系统模型参数的无偏估计.并将提出的方法与递推增广最小二乘算法和递推广义增广最小二乘算法进行了比较研究;用仿真试验分析了这些算法的各自特点和适用范围. |
| 英文摘要 |
| For Box-Jenkins systems with correlated noises, a bias compensation least squares (BCLS) identification method is proposed by means of the bias compensation principle. The analysis is then given to show that the BCLS algorithm can give the unbiased estimates of the system model parameters. Finally, the advantages of the proposed BCLS algorithm over the recursive extended least squares algorithm and recursive generalized extended least squares algorithm are shown by using simulation tests. |