| 引用本文: | 张友民 李庆国 戴冠中 张洪才.基于奇异值分解的递推辨识方法[J].控制理论与应用,1995,12(2):224~229.[点击复制] |
| ZHANG Youmin, LI Qingguo, DAI Guanzhong and ZHANG Hongcai.A New Recursive Identification Method Based On Singular Value Decomposition[J].Control Theory and Technology,1995,12(2):224~229.[点击复制] |
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| 基于奇异值分解的递推辨识方法 |
| A New Recursive Identification Method Based On Singular Value Decomposition |
| 摘要点击 1183 全文点击 470 投稿时间：1994-02-14 修订日期：1994-08-05 |
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| DOI编号 |
| 1995,12(2):224-229 |
| 中文关键词 奇异值分解 递推最小二乘辨识 无偏估计 持续激励 |
| 英文关键词 singular value decomposition recursive least square identification un-biased estimation persisten excitation |
| 基金项目 |
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| 中文摘要 |
| 本文提出一种基于奇异值分解（SVD）的递推最小二乘辨识新方法，该方法不仅有很好的收敛性和数值稳定性，而且在系统的输入信号不满足持续激励的充分必要条件下，仍能得到系统参数的无偏估计，仿真计算结果证明了本文方法的有效性和优越性。 |
| 英文摘要 |
| Based on singular value decomposition（SVD）, this paper develops a recursive least square identification method, which takes in account input excitation. It is demonstrated that the SVD-based approach proposed in here can not only obviously improve the convergency rate, numerical stability of RLS, but also provide much more precise identification results and greatly enhance the robustness of the system identification. More over, this algorithm is formulated in the form of vector-matrix and matrix- matrix operations, so it is useful for parallel computers. |