| 损失数据线性参数系统的递推最小二乘辨识方法 |
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| 引用本文： | 丁锋,汪菲菲.损失数据线性参数系统的递推最小二乘辨识方法[J].控制与决策,2016,31(12):2261-2266. |
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| 作者姓名： | 丁锋 汪菲菲 |
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| 作者单位： | 江南大学物联网工程学院,江苏无锡214122,江南大学物联网工程学院,江苏无锡214122 |
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| 基金项目： | 国家自然科学基金项目(61273194) |
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| 摘 要： | 针对损失数据线性参数系统的参数辨识问题, 借助辅助模型辨识思想推导出其变递推间隔辅助模型递 推最小二乘算法.为了提高该算法的计算效率, 利用分解技术得到变递推间隔分解递推最小二乘算法 估计系统参数.此外, 在变递推间隔分解递推最小二乘算法中引入遗忘因子, 从而提高参数估计精度和收敛速度.仿真结果表明, 所提出的算法能有效估计系统参数.
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| 关 键 词： | 参数估计 辅助模型 分解技术 损失数据 线性参数系统 |
| 收稿时间： | 2015/11/3 0:00:00 |
| 修稿时间： | 2015/11/3 0:00:00 |
Recursive least squares identification algorithms for linear-in-parameter systems with missing data |
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| DING Feng and WANG Fei-fei.Recursive least squares identification algorithms for linear-in-parameter systems with missing data[J].Control and Decision,2016,31(12):2261-2266. |
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| Authors: | DING Feng and WANG Fei-fei |
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| Abstract: | An interval-varying auxiliary model based recursive least squares(AM-RLS) algorithm is derived for linear-in-parameter systems with missing data by means of the auxiliary model identification idea.In order to improve the computation efficiency, an interval-varying decomposition AM-RLS algorithm is proposed for estimating the system parameters.The introduction of the forgetting factors can improve the parameter estimation accuracy and accelerate the convergence rates of the interval-varying decomposition based AM-RLS algorithm.The simulation results show that the proposed algorithms can effectively estimate the parameters of the system. |
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