RLS和LMS算法分析比较

RLS和LMS自适应滤波器的性能对比，代码在MATLAB中完美运行。

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%LMS自适应滤波器性能分析 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear 
clc
close all
%%%%%%%%%%%%%%参数设置%%%%%%%%%%%%%%%%%%%%%%%
w=3.2;
step_len=0.075;
variance_v=0.001;
repeat_times=200;
iteration_times=500;
filt_len=2;
delay=fix（filt_len/2）-1;
%%%%%%%%%%%%%%%%%%%%变量及数组初始化%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Chan_factor=（1+cos（2*pi*（[1:1:3]-2）/w））/2;
Square_err=zeros（repeat_timesiteration_times）;
%%%%%%%%%%%%%外循环，重复做repeat_timws次实验，平均值%%%%%%%%%%%%%%%%
for loop1=1:repeat_times
%%%%%%%%%%%%%%内循环变量及数组初始化%%%%%%%%%%%%%%%%%%%%%%%%
Source=zeros（iteration_times1）;
W=zeros（filt_len1）;
%%%%%%%%%%%%%内循环，做iteration_times次迭代%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   for i=1:iteration_times
%%%%%%%%%%%%%%信源，产生等概率分布的正负1%%%%%%%%%%%%%%%%%%%%%%%%%%%%
      if rand<0.5
          Source（i）=-1;
      else
          Source（i）=1;
      end
   end
%%%%%%%%%%%%%%信道，滤波和加噪声%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   Revdata=filter（Chan_factor1Source）+randn（iteration_times1）*sqrt（variance_v）;
%%%%%%%%%%%%%%均衡器，基于LMS算法%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%    
    for loop2=1:iteration_times-filt_len+1
        U=Revdata（loop2:loop2+filt_len-1）;
        d=Source（loop2+delay）;
        
        e=d-W‘*U;
        W=W+step_len*U*e;
        
        Square_err（loop1loop2）=e.^2;
    end
end
%%%%%%%%%%%%%%作图，显示结果%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Square_err_mean=sum（Square_err（::））/repeat_times;
semilogy（Square_err_mean）;
figure（2）;
stem（W）;

 属性            大小     日期    时间   名称
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     文件       1586  2008-01-10 05:07  RLS与LMS算法分析及比较\.m

     文件       1749  2007-12-23 22:36  RLS与LMS算法分析及比较\RLSnew.m

     文件        736  2017-06-10 20:26  RLS与LMS算法分析及比较\rls_lms.m

     目录          0  2017-09-13 14:16  RLS与LMS算法分析及比较

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