rls5.m

这是在国外某网站上下的一些自适应信号处理的源程序

%RLS5 Problem 4.5
%
%   'ifile.mat' - input file containing:
%      I - members of ensemble
%      K - iterations
%      s - deterministic part of reference signal
%      sigman - standard deviation of noise in reference signal
%      ND - delayer order
%      Ha, Hb - numerator, denominator of H(z)
%      sigmax - standard deviation of input
%         (obtainable from sigman and H(z))
%      N - filter order
%      lambda - forgetting factor
% 
%   'ofile.mat' - output file containing:
%      ind - sample indexes 
%      MSE - mean-square error
%      ep - last experiment a priori error  

clear all	% clear memory
load ifile;	% read input variables
LD=ND+1;	% delayer length
L=N+1;		% filter length
delta=1/sigmax^2;	% initialization factor for SD
ilambda=1/lambda;	% auxiliary constant
MSE=zeros(K,1);		% prepare to accumulate MSE*I

for i=1:I,		% ensemble
   D=zeros(LD,1);	% initial delayer memory  
   X=zeros(L,1);    	% initial memory
   SD=delta*eye(L);	
	% initial inverse of deterministic correlation matrix
   W=zeros(L,1);	% initial coefficient vector
   n=randn(K,1)*sigman;	% noise in reference signal 
   x=filter(Ha,Hb,n);   % input   
   for k=1:K,		% iterations
      D=[s(k)+n(k)
         D(1:ND)];	% new delay vector
      d=D(LD);		% desired signal sample
      X=[x(k)		
         X(1:N)];	% new input vector
      ep(k)=d-W'*X;	% a priori error sample
      psi=SD*X;
      SD=ilambda*(SD-psi*psi'/(lambda+psi'*X));
	% new inverse of deterministic correlation matrix
      W=W+ep(k)*SD*X;		% new coefficient vector
      MSE(k)=MSE(k)+(ep(k))^2;	% accumulate MSE*I
   end
end

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