Robust Least Square Method and Its Application to Parameter Estimation

Author

Mei, Zhang ; Chenghui, Zhang ; Huanshui, Zhang ; Peng, Cui ; Du Yanchun

fYear

2007

fDate

18-21 Aug. 2007

Firstpage

1483

Lastpage

1486

Abstract

The system identification problem is researched when the input and output signal are both corrupted by noise. The robust least square (RLS) method and its application to parameter estimation problem, in which the perturbations are unknown but bounded (UBB), are introduced. The method can be interpreted as Tikhonov regularization procedure, with the advantage that it provides an exact bound on the robustness of solution and a rigorous way to compute the regularization parameter. Simulation results verify that the estimation precision and the robustness anti-noise of RLS are remarkably higher than other method when the input and output signal are both corrupted by noise.

Keywords

least squares approximations; parameter estimation; perturbation techniques; parameter estimation; regularization parameter; robust least square method; robustness anti-noise; system identification; Automation; Equations; Least squares approximation; Least squares methods; Logistics; Noise robustness; Parameter estimation; Resonance light scattering; System identification; Upper bound; Parameter estimation; Robust least square method(RLS); Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation and Logistics, 2007 IEEE International Conference on

Conference_Location

Jinan

Print_ISBN

978-1-4244-1531-1

Type

conf

DOI

10.1109/ICAL.2007.4338805

Filename

4338805