Convergence of forgetting factor least square algorithms

被引：4

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：

time-varying systems; identification; parameter estimation; least squares;

D O I：

10.1109/PACRIM.2001.953662

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Convergence of the forgetting factor least square algorithm (FFLS) is analyzed by using stochastic process theory; and the upper bound of the parameter estimation error is derived. For timevarying stochastic systems, the FFLS algorithm is capable of tracking the time-varying parameters and the parameter estimation error is bounded. The upper bound of the parameter estimation error can be minimized by choosing the forgetting factor properly. Simulated results obtained support the theoretical findings.

引用

页码：433 / 436

页数：4

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