A q-Noise Constrained Least Mean Square Algorithm

被引：1
|
作者：
Bin Saeed, Muhammad O. [1 ]
Zerguine, Azzedine [2 ]
机构：
[1] Air Univ, Dept Elect & Comp Engn, Islamabad, Pakistan
[2] King Fahd Univ Petr & Minerals, Elect Engn Dept, Dhahran 31261, Saudi Arabia
来源：
2020 54TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS | 2020年
关键词：
qq-Derivative; Noise constrained algorithm; least mean square algorithm;
D O I：
10.1109/IEEECONF51394.2020.9443480
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The Least Mean Square (LMS) algorithm has an inherent trade-off issue between convergence speed and steady-state error performance. One of the algorithms proposed to tackle this issue is called the Noise Constrained LMS algorithm, which uses the noise variance to iteratively vary the stepsize. This work uses the q-derivative to propose an improved Noise Constrained LMS algorithm. Simulation results show that the proposed algorithm shows better performance than the conventional algorithm at the cost of only a minimal increase in complexity. Steady-state analysis for the proposed algorithm has also been carried out.
引用
收藏
页码：1420 / 1424
页数：5
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