LSM&RLS;算法对比

close all; 
clear; 
clc;
repeat_times=50;                              %重复次数，平滑误差
iters=600;                                    %迭代次数
p=2;                                          %滤波器抽头数
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%序列x（iters）由零均值，单位方差的白噪声通过一个二阶回归方程
%x（iters）+a1x（iters-1）+a2x（iters-2）=v（iters）
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a1=-1.6; a2=0.8; sigma_v=1;
u=0.002;                                     %LMS自适应步长
forget_factor=0.98;                          %遗忘因子lamda
LMS_w1=zeros（repeat_timesiters）;
RLS_w1=zeros（repeat_timesiters）;
for k=1:repeat_times     
    xtemp=zeros（iters+p1）;                   %初始化序列，x（iters）为列向量   
    vn=normrnd（0sigma_viters+p1）;          %零均值，单位方差的高斯白噪声
    for i=1:iters     
        xtemp（i+2）=vn（i）-a1*xtemp（i+1）-a2*xtemp（i）;
    end
    x=xtemp（p+1:length（xtemp））;               %产生2阶自回归信号，剔除初始的前两个零值
    x=[zeros（p1）;x];
    w_e=zeros（piters）;                       %初始化LMS权矢量
    W_e=zeros（piters）;                       %初始化RLS权矢量         
    T=10*eye（p）;                              %初始化T（n）=1/R（n）R（n）为随机信号的自相关函数
    Pi=zeros（p1）;                            %Pi（n）=T（n-1）*x（n）
    K=zeros（p1）;