Convergence and stability of recursive damped least square algorithm

被引：0

作者：

Zengqiang C. ^{[1
]}

Maoqiong L. ^{[1
]}

Zhuzhi Y. ^{[1
]}

机构：

[1] Department of Computer and System Science, Nankai University

来源：

Applied Mathematics and Mechanics | 2000年 / 21卷 / 2期

关键词：

Convergence; Damped least square; Recursive algorithm; Stability; System identification;

D O I：

10.1007/BF02458525

中图分类号：

学科分类号：

摘要：

The recursive least square is widely used in parameter identification. But it is easy to bring about the phenomena of parameters burst-off. A convergence analysis of a more stable identification algorithm-recursive damped least square is proposed. This is done by normalizing the measurement vector entering into the identification algorithm. It is shown that the parametric distance converges to a zero mean random variable. It is also shown that under persistent excitation condition, the condition number of the adaptation gain matrix is bounded, and the variance of the parametric distance is bounded.

引用

页码：237 / 242

页数：5

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