一种类箕舌线函数的变步长归一化自适应滤波算法

火元莲; 龙小强; 连培君; 王丹凤

doi:10.11999/JEIT191003

一种类箕舌线函数的变步长归一化自适应滤波算法

doi: 10.11999/JEIT191003

西北师范大学物理与电子工程学院兰州 730070

基金项目: 国家自然科学基金(61561044)

详细信息

作者简介:
火元莲：女，1973年生，博士，副教授，研究方向为信号与信息处理、数字图像处理

龙小强：男，1995年生，硕士生，研究方向为自适应滤波技术

连培君：女，1995年生，硕士生，研究方向为自适应滤波技术

王丹凤：女，1996年生，硕士生，研究方向为自适应滤波技术

通讯作者:
龙小强　lxqchj@163.com

中图分类号: TN713
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- 被引次数: 0
出版历程
- 收稿日期: 2019-12-16
- 修回日期: 2020-10-24
- 网络出版日期: 2020-12-07
- 刊出日期: 2021-02-23

A Kind of Versoria Function Normalized Adaptive Filtering Algorithm

College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China

Funds: The National Natural Science Foundation of China (61561044)

摘要

摘要:
在综合考虑自适应滤波算法设计中收敛速度、稳态误差、计算复杂度和跟踪性能等指标的基础上，该文提出一种类箕舌线函数的变步长归一化自适应滤波算法，用类箕舌线函数代替Sigmoid函数作为步长迭代公式，引入基于相关误差的变步长调整原则，在大大增强算法稳定性的同时大幅度提升了算法的收敛速度、跟踪性能，减小了算法的计算复杂度。在Matlab平台上分析了改进的步长函数中参数
\begin{document}$\alpha $\end{document}
,
以及
的不同取值对算法的影响，并将该文算法与已有的基于Sigmoid函数和基于箕舌线函数的变步长LMS算法进行了比较，仿真结果表明，该文算法有更快的收敛速度、更好的跟踪能力以及较小的稳态误差和较强的鲁棒性。
- 自适应滤波 /
- 变步长自适应算法 /
- 最小均方算法 /
- 箕舌线函数
Abstract:
On the basis of the comprehensive consideration in the design of the indexs of the adaptive filter algorithm convergence speed, steady-state error, computational complexity and tracking performance, a kind of Versoria function normalized adaptive filtering algorithm is proposed in this paper. The class Versoria function is used instead of Sigmoid function as step iterative formula, introducing variable step size based on the relevant error adjustment principle, the stability of the algorithm is enhanced greatly. At the same time, the convergence speed and tracking performance of the algorithm is promoted and the computational complexity of the algorithm is reduced. The influence of the parameter
\begin{document}$\alpha $\end{document}
,
and
different value of the step function of algorithm is analyzed on Matlab platform. Compared with the Sigmoid function variable step size LMS algorithm and variable step size LMS algorithm based on Versoria function, and the simulation results show that this algorithm has faster convergence speed, better tracking ability, smaller steady-state error and strong robustness.
- Adaptive filtering /
- Variable step size adaptive algorithm /
- LMS algorithm /
- Versoria function

HTML全文

图 1 类箕舌线函数曲线调整过程曲线图

下载: 全尺寸图片幻灯片

图 2 步长因子$\mu \left( n \right)$与误差${{e}}\left( n \right)$调整曲线图

下载: 全尺寸图片幻灯片

图 3 $\gamma $不同时算法收敛速度图

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图 4 5种算法步长调整曲线图

下载: 全尺寸图片幻灯片

图 5 不同信噪比背景下5种算法MSE对比图

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图 6 不同信噪比背景下5种算法面对时变系统时的跟踪性能对比图

下载: 全尺寸图片幻灯片

表 1 不同信噪比下算法收敛次数比较

信噪比(dB) SVSLMS算法文献[15]算法文献[16]算法文献[17]算法本文算法

10 250 200 450 210 25
20 300 250 900 320 30
30 400 270 1000 400 35

下载: 导出CSV

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