Mean square convergence of multi-innovation forgetting gradient identification

被引：2

作者：

Ding, F ^{[1
]}

Ding, T ^{[1
]}

机构：

[1] Tsing Hua Univ, Dept Automat, Beijing 100084, Peoples R China

来源：

2001 IEEE PACIFIC RIM CONFERENCE ON COMMUNICATIONS, COMPUTERS AND SIGNAL PROCESSING, VOLS I AND II, CONFERENCE PROCEEDINGS | 2001年

关键词：

identification; parameter estimation; signal processing;

D O I：

10.1109/PACRIM.2001.953663

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a multi-innovation forgetting gradient identification method is studied; and its mean square convergence is analyzed by using stochastic process theory. The analysis indicates that the stationary data can improve accuracy of the parameter estimates, and that a way to choose the forgetting factor is obtained to minimize an upper bound of the parameter estimation error. The multi-innovation forgetting gradient algorithm is capable of reducing the effect of poor data in parameter estimation, having good robustness, and tracking time-varying parameters.

引用

页码：437 / 440

页数：4