Convergence of forgetting factor least square algorithms

Author

Ding, Feng ; Ding, Tao

Author_Institution

Dept. of Autom., Tsinghua Univ., Beijing, China

Volume

2

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

433

Abstract

Convergence of the forgetting factor least square (FFLS) algorithm is analyzed by using stochastic process theory; and the upper bound of the parameter estimation error is derived. For time-varying 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

Keywords

convergence of numerical methods; least squares approximations; parameter estimation; signal processing; stochastic processes; stochastic systems; error upper bound; forgetting factor least square algorithms; parameter estimation; parameter identification; signal processing; stochastic process theory; time-varying stochastic systems; Automation; Convergence; Covariance matrix; Least squares approximation; Least squares methods; Parameter estimation; Stochastic processes; Stochastic systems; Time varying systems; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications, Computers and signal Processing, 2001. PACRIM. 2001 IEEE Pacific Rim Conference on

Conference_Location

Victoria, BC

Print_ISBN

0-7803-7080-5

Type

conf

DOI

10.1109/PACRIM.2001.953662

Filename

953662