基于奇异值分解的递推辨识方法  

A New Recursive Identification Method Based On Singular Value Decomposition

作　　者：张友民[1] 李庆国[1] 戴冠中[1] 张洪才[1] 

机构地区：[1]西北工业大学自动控制系

出　　处：《控制理论与应用》1995年第2期224-229,共6页Control Theory & Applications

基　　金：国家自然科学基金

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．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 also useful for parallel computers.

关 键 词：奇异值分解 最小二乘辨识 无偏估计 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]