RLS lattice algorithm using gradient based variable forgetting factor

Author

So, C.F. ; Ng, S.C. ; Leung, S.H.

Author_Institution

Dept. of Electr. Eng., Hong Kong Polytech. Univ., China

Volume

2

fYear

2003

fDate

20-24 July 2003

Firstpage

1168

Abstract

A gradient based variable forgetting factor (GVFF) RLS lattice (RLSL) algorithm is introduced in this paper. The steepest descent approach is used to control the forgetting factor which is based on the dynamic equation of the gradient of the mean square error. Compared with the standard RLSL algorithm, GVFF-RLSL algorithm gives fast tracking with a small mean square model error and its performance is not degraded much even in low signal-to-noise ratios (SNR) for time varying systems.

Keywords

adaptive signal processing; gradient methods; lattice filters; least squares approximations; mean square error methods; recursive estimation; recursive filters; time-varying systems; dynamic equation; fast tracking; forgetting factor control; gradient based variable forgetting factor; low signal-to-noise ratios; recursive least squares lattice algorithm; steepest descent approach; time varying systems; Computational complexity; Degradation; Equations; Filters; Lattices; Least squares methods; Mean square error methods; Resonance light scattering; Steady-state; Time varying systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223857

Filename

1223857