RLS Lattice Algorithm Using Gradient Based Variable Forgetting Factor

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

RLS Lattice Algorithm Using Gradient Based Variable Forgetting Factor

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

Hong Kong Metropolitan University

Research output: Contribution to conference › Paper › peer-review

4 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 will not be degraded much even in low signal-to-noise ratios (SNR) for time varying system.

Original language	English
Pages	1168-1172
Number of pages	5
Publication status	Published - 2003
Event	International Joint Conference on Neural Networks 2003 - Portland, OR, United States Duration: 20 Jul 2003 → 24 Jul 2003

Conference

Conference	International Joint Conference on Neural Networks 2003
Country/Territory	United States
City	Portland, OR
Period	20/07/03 → 24/07/03

Cite this

@conference{ea3faca4f1504181a325048f2eea1582,

title = "RLS Lattice Algorithm Using Gradient Based Variable Forgetting Factor",

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 will not be degraded much even in low signal-to-noise ratios (SNR) for time varying system.",

author = "So, \{C. F.\} and Ng, \{S. C.\} and Leung, \{S. H.\}",

year = "2003",

language = "English",

pages = "1168--1172",

note = "International Joint Conference on Neural Networks 2003 ; Conference date: 20-07-2003 Through 24-07-2003",

}

TY - CONF

T1 - RLS Lattice Algorithm Using Gradient Based Variable Forgetting Factor

AU - So, C. F.

AU - Ng, S. C.

AU - Leung, S. H.

PY - 2003

Y1 - 2003

N2 - 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 will not be degraded much even in low signal-to-noise ratios (SNR) for time varying system.

AB - 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 will not be degraded much even in low signal-to-noise ratios (SNR) for time varying system.

UR - https://www.scopus.com/pages/publications/0141572289

M3 - Paper

AN - SCOPUS:0141572289

SP - 1168

EP - 1172

T2 - International Joint Conference on Neural Networks 2003

Y2 - 20 July 2003 through 24 July 2003

ER -

RLS Lattice Algorithm Using Gradient Based Variable Forgetting Factor

Abstract

Conference

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