RLS lattice algorithm using gradient based variable forgetting factor

Chi Fuk Henry SO, S. C. NG, S. H. LEUNG

Research output: Chapter in Book/Report/Conference proceedingChapters

3 Citations (Scopus)

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. Copyright © 2003 by The Institute of Electrical and Electronics Engineers, Inc.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks 2003
Place of PublicationUSA
PublisherIEEE
Pages1168-1172
Volume2
ISBN (Print)0780378989
DOIs
Publication statusPublished - 2003

Citation

So, C. F., Ng, S. C., & Leung, S. H. (2003). RLS lattice algorithm using gradient based variable forgetting factor. In Proceedings of the International Joint Conference on Neural Networks 2003 (Vol. 2, pp. 1168-1172). USA: IEEE.

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