摘要
传统LMS算法的优点是计算简单、易于实现，缺点是收敛速度慢，如果为加快收敛速度而增大步长因子μ，则会导致大的稳态误差，甚至引起算法发散。固定步长因子无法解决收敛速度和稳态误差之间的矛盾。本文通过建立步长因子μ与误差信号之间的非线性函数关系，得出一种新的变步长自适应滤波算法（SVSLMS）。理论分析和计算机仿真结果表明该算法的性能优于传统的LMS算法和NLMS算法。即在计算量增加不多的前提下，能同时获得较快的收敛、跟踪速度和较小的稳态误差。
Although classical LMS algorithm has simple complexity and easy to be implemented, its convergence sPeed is very slow. In order to speedup the convergence process, one can increase the step size,but at the same time, the steady state error will be large and even the algorithm may hecDme unstable.Fixed step size can not result in fast convergence speed and low residual error simultaneously. By building a nonlinear function relationship between μ andthe error signal, this paper presents a novel variablestep size adaptive filtering algorithm, i. e. SVSLMS.The excellent properties of the SVSLMS algorithm are confirmed by theoretical analysis and computer simulations.
出处
《数据采集与处理》
CSCD
1997年第3期171-174,共4页
Journal of Data Acquisition and Processing
基金
国家自然科学基金
国家教委博士点基金
广东省自然科学基金
关键词
算法
自适应滤波
LMS算法
信号处理
algorithms
adaptive filtering
LMS algorithm
NLMS algorithm
Sigmoid function
