.png){kind=icon}
Return
A two-gain NLMS algorithm for sparse system identification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2022.108636.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper introduces a modified normalized least-mean-square (NLMS) algorithm for sparse system identification. The proposed approach is in line with the proportionate NLMS (PNLMS)-type algorithms in the sense that different gains are considered in the coefficient update equation. However, in contrast to the PNLMS-type algorithms, the proposed approach considers only two different gains, one related to the active coefficients and other related to the inactive ones. Such an approach allows obtaining closed form expressions for both gains without relying on proportionality functions and activation factors. As a result of the proposed strategy, the new algorithm, termed here two-gain NLMS (TG-NLMS), leads to both fast convergence and low computational complexity. Simulation results are shown aiming to confirm the effectiveness of the proposed algorithm.
Keywords:
Adaptive filtering
PNLMS-type algorithms
Sparse NLMS algorithm
Steady-state behavior
System identification
Journal
IF:
3.6
Papers:
9.9K
Citations:
1.7W
Organization
Cited Papers
.png){kind=icon}
ShareSaveCorrelation and prediction of solute transfer to chloroalkanes from both water and the gas phase
OAAI
OAAIShareSaveShareSaveShareSaveShareSaveShareSave
View more