基于系数估值约束的改进LMS自适应滤波算法  

Improved LMS adaptive filtering algorithm based on parameter estimation constraint

作　　者：王艳[1,2] Wang Yan(School of Finance,X V an Eurasia University,X V an 710061,..China;School of Software Engineering,Xi'an Jiaotong University,Xi'an 710061,China)

机构地区：[1]西安欧亚学院金融学院,西安710061 [2]西安交通大学软件学院,西安710061

出　　处：《电测与仪表》2018年第16期42-46,共5页Electrical Measurement & Instrumentation

摘　　要：为提高扩散LMS自适应滤波算法的收敛速度和保持较低的稳态误差,在扩散LMS算法基础上,提出一种基于参数估计值约束的分布式自适应网络滤波算法,算法在迭代收敛过程中,根据相邻迭代过程参数估计值差值约束实现自适应的调整步长大小,从而使得算法在估计初期采用较大步长以加速收敛,而在估计后期自适应的调整步长以保持较低的稳态误差。对比实验结果表明:相比于现有其他算法,所提算法在进行分布式估计时性能更优。In order to design a fast-convergence,low steady-state error and robust adaptive filtering algorithm,on the basis of the diffuse LMS algorithm,a distributed adaptive network filtering algorithm based on the unknown parameter estimation constraint was proposed in this paper.During the iterative convergence process,the step size was adaptively adjusted according to the difference norm of parameter estimation between adjacent iterations.Thus,a large step size was used to accelerate the convergence at a faster speed in the initial estimation period,and an adaptive adjustment step length was used in the later period to maintain a relatively low steady-state error.Experimental results show that,the proposed algorithm has better performance on distributed estimation comparing with other existing algorithms.

关 键 词：分布式估计 脉冲噪声 参数估计值约束 变步长 

分 类 号：TM933[电气工程—电力电子与电力传动]