normalized_lms.m

LMS 神经网络算法

  clear all;
          snr=20;
          order=8;
          Hn =[0.8783   -0.5806    0.6537   -0.3223    0.6577   -0.0582   0.2895   -0.2710    0.1278   -0.1508    0.0238   -0.1814   0.2519   -0.0396    0.0423   -0.0152    0.1664   -0.0245   0.1463   -0.0770    0.1304   -0.0148    0.0054   -0.0381    0.0374   -0.0329    0.0313   -0.0253    0.0552  -0.0369   0.0479   -0.0073    0.0305   -0.0138    0.0152   -0.0012  0.0154   -0.0092    0.0177   -0.0161    0.0070   -0.0042  0.0051   -0.0131    0.0059   -0.0041    0.0077   -0.0034   0.0074   -0.0014    0.0025   -0.0056    0.0028   -0.0005   0.0033   -0.0000    0.0022   -0.0032    0.0012   -0.0020   0.0017   -0.0022    0.0004   -0.0011      0          0   ];
          Hn=Hn(1:order);
          N=1000;
%           r=wavread('C:\Matlab\work\Write_In_Paper\Shi_Yan.wav');
%           r=r(1:N); 
%           r=awgn(r,snr,'measured'); 
          EE=zeros(N,1);   Loop=150;  mu=0.3;
   for   nn=1:Loop
          r=sign(rand(N,1)-0.5); % r=awgn(r,snr,'measured');
          output=conv(r,Hn); 
          output=awgn(output,snr,'measured');
          win=zeros(1,order); 
          error=zeros(1,N)';
  
     for i=order:N
         input=r(i:-1:i-order+1); 
         e=output(i)-win*input;
         win=win+mu*e*input'/(1+input'*input);
         error(i)=error(i)+e^2;
     end;
     EE=EE+error;
 end;  
            error=EE/Loop;
     
             figure;  
             error=10*log10(error(order:N));
             plot(error,'r');  axis tight; grid on;
%              semilogy(error(order:N),'b'); grid; axis  tight;
%              subplot(212);
%              plot(win,'r'); 
%              hold on; plot(Hn,'b'); grid;  axis tight;





 snr=20;
          order=8;
          Hn =[0.8783   -0.5806    0.6537   -0.3223    0.6577   -0.0582   0.2895   -0.2710    0.1278   -0.1508    0.0238   -0.1814   0.2519   -0.0396    0.0423   -0.0152    0.1664   -0.0245   0.1463   -0.0770    0.1304   -0.0148    0.0054   -0.0381    0.0374   -0.0329    0.0313   -0.0253    0.0552  -0.0369   0.0479   -0.0073    0.0305   -0.0138    0.0152   -0.0012  0.0154   -0.0092    0.0177   -0.0161    0.0070   -0.0042  0.0051   -0.0131    0.0059   -0.0041    0.0077   -0.0034   0.0074   -0.0014    0.0025   -0.0056    0.0028   -0.0005   0.0033   -0.0000    0.0022   -0.0032    0.0012   -0.0020   0.0017   -0.0022    0.0004   -0.0011      0          0   ];
          Hn=Hn(1:order);
          N=1000;
%           r=wavread('C:\Matlab\work\Write_In_Paper\Shi_Yan.wav');
%           r=r(1:N); 
%           r=awgn(r,snr,'measured'); 
          EE=zeros(N,1);   Loop=150;  mu=0.01;
   for   nn=1:Loop
          r=sign(rand(N,1)-0.5); % r=awgn(r,snr,'measured');
          output=conv(r,Hn); 
          output=awgn(output,snr,'measured');
          win=zeros(1,order); 
          error=zeros(1,N)';
  
     for i=order:N
         input=r(i:-1:i-order+1); 
         e=output(i)-win*input;
         win=win+mu*e*input';
         error(i)=error(i)+e^2;
     end;
     EE=EE+error;
 end;  
            error=EE/Loop;
     
            hold on;
             error=10*log10(error(order:N));
             plot(error);  axis tight; grid on;
%              semilogy(error(order:N),'r'); grid; axis  tight;
%              subplot(212);
%              plot(win,'r'); 
%              hold on; plot(Hn,'b'); grid;  axis tight;