lms4.m

这是自适应信号处理的几个matlab程序

%LMS4 Problem 1.1.2.1
%
%   'ifile.mat' - input file containing:
%      I - members of ensemble
%      K - iterations
%      sigmax - standard deviation of input
%      lambdaW, sigmaW - parameters of first-order Markov 
%                        processes which generate Wo
%      Wo0 - initial coefficient vector of plant
%      sigman - standard deviation of measurement noise
%      mu - convergence factor
% 
%   'ofile.mat' - output file containing:
%      ind - sample indexes 
%      texcMSE - total excess MSE 

clear all	% clear memory
load ifile;	% read input variables
L=length(Wo0);		% plant and filter length
N=L-1;			% plant and filter order
MSE=zeros(K,1);		% prepare to accumulate MSE*I
MSEmin=zeros(K,1);	% prepare to accumulate minMSE*I

for i=1:I,		% ensemble
   X=zeros(L,1);    	% initial memory
   Wo=Wo0;		% initial coefficient vector of plant
   W=zeros(L,1);	% initial coefficient vector
   x=randn(K,1)*sigmax;		% input 
   nW=randn(L,K)*sigmaW;
 	% input to Markov processes which generate Wo 
   n=randn(K,1)*sigman;		% measurement noise
   for k=1:K,		% iterations
      X=[x(k)		
         X(1:N)];	% new input vector
      d=Wo'*X;		% desired signal sample
      y=W'*X;		% output sample
      e=d+n(k)-y;	% error sample
      Wo=lambdaW*Wo+nW(:,k);	
			% new coefficient vector of plant
      W=W+2*mu*e*X;	% new coefficient vector
      MSE(k)=MSE(k)+e^2;	% accumulate MSE*I
      MSEmin(k)=MSEmin(k)+(n(k))^2;	% accumulate MSEmin*I
   end
end

ind=0:(K-1);		% sample indexes
texcMSE=(MSE-MSEmin)/I;		% calculate total excess MSE
save ofile ind texcMSE;		% write output variables