Title :
Robust Least Square Method and Its Application to Parameter Estimation
Author :
Mei, Zhang ; Chenghui, Zhang ; Huanshui, Zhang ; Peng, Cui ; Du Yanchun
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;
Conference_Titel :
Automation and Logistics, 2007 IEEE International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-1531-1
DOI :
10.1109/ICAL.2007.4338805
