| 引用本文: | 丁 锋,萧德云,丁 韬.多新息随机梯度辨识方法[J].控制理论与应用,2003,20(6):870~874.[点击复制] |
| DING Feng,XIAO De-yun,DING Tao.Multi-innovation stochastic gradient identification method[J].Control Theory and Technology,2003,20(6):870~874.[点击复制] |
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| 多新息随机梯度辨识方法 |
| Multi-innovation stochastic gradient identification method |
| 摘要点击 2484 全文点击 2308 投稿时间：2001-11-01 修订日期：2002-11-22 |
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| DOI编号 10.7641/j.issn.1000-8152.2003.6.011 |
| 2003,20(6):870-874 |
| 中文关键词 辨识 参数估计 多新息辨识 最小均方算法 |
| 英文关键词 identification parameter estimation multi-innovation identification least mean square algorithm |
| 基金项目 国家自然科学基金项目(60074029); 国家自然科学基金重点项目(69934010); 清华大学信息学院创新基金项目. |
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| 中文摘要 |
| 多新息随机梯度辨识方法是系统辨识和参数估计的一种基本方法.该方法由于采用了间断迭代,因此可以克服坏数据对参数估计的影响,且具有较强的鲁棒性,又可以跟踪时变参数.作者从理论上给出了多新息随机梯度辨识方法的推导过程,同时列出多新息随机梯度辨识方法的各种变形.数字仿真实验表明多新息随机梯度辨识方法具有良好的性能. |
| 英文摘要 |
| Multi-innovation stochastic gradient identification algorithm is one of the basic methods in the area of system (identification) and parameter estimation. It can overcome the effect of bad data on parameter estimation, and has strong robustness, and can track time-varying parameters because the discontinuous recursive computation was applied. By using a dimension search, the multi-innovation stochastic gradient identification was derived via minimizing the criteria, and some derivation algorithms from the multi-innovation stochastic gradient identification were given. The numeric simulation experiments indicated that the proposed algorithm has good performance. |