Set-membership binormalized data-reusing LMS algorithms

Authors:
P.S.R. Diniz;S. Werner
Affiliations:
COPPE, Fed. Univ. of Rio de Janeiro, Brazil;-
Venue:
IEEE Transactions on Signal Processing
Year:
2003

Citing 0
Cited 9

Membership set identification with periodic inputs and orthonormal regressors

Signal Processing - Special section: Multimodal human-computer interfaces
Set-membership proportionate affine projection algorithms

EURASIP Journal on Audio, Speech, and Music Processing
Selective partial update and set-membership subband adaptive filters

Signal Processing
Convergence analysis of adaptive DS-CDMA receivers in multi-path channels

Signal Processing
Steady-state mean-square performance analysis of a relaxed set-membership NLMS algorithm by the energy conservation argument

IEEE Transactions on Signal Processing
Improved Quasi-Newton adaptive-filtering algorithm

IEEE Transactions on Circuits and Systems Part I: Regular Papers
Steady-state mean squared error and tracking performance analysis of the quasi-OBE algorithm

Signal Processing
Modified quasi-OBE algorithm with improved numerical properties

Signal Processing
A linear recurrent kernel online learning algorithm with sparse updates

Neural Networks

Quantified Score

Hi-index	35.69

Visualization

Abstract

This paper presents and analyzes novel data selective normalized adaptive filtering algorithms with two data reuses. The algorithms [the set-membership binormalized LMS (SM-BN-DRLMS) algorithms] are derived using the concept of set-membership filtering (SMF). These algorithms can be regarded as generalizations of the previously proposed set-membership NLMS (SM-NLMS) algorithm. They include two constraint sets in order to construct a space of feasible solutions for the coefficient updates. The algorithms include data-dependent step sizes that provide fast convergence and low-excess mean-squared error (MSE). Convergence analyzes in the mean squared sense are presented, and closed-form expressions are given for both white and colored input signals. Simulation results show good performance of the algorithms in terms of convergence speed, final misadjustment, and reduced computational complexity.