brls.m

一个matlab实现IIR、FIR、LMS、NLMS等算法的m代码

%BRLS Bilinear RLS algorithm
%
%   'ifile.mat' - input file containing:
%      Nr - members of ensemble
%      dim - iterations
%      Sx - standard deviation of input
%      Sn - standard deviation of measurement noise
%      lambda - exponential weighting factor
%      
%   'ofile.mat' - output file containing:
%      MSE - mean-square error


clear all		% clear memory
load ifile;		% read input variables

for j=1:Nr
   n=Sn*randn(dim,1);        % noise at system output   
   x=Sx*(randn(dim,1));         % input signal
   xl(1)=0; xl(2:dim)=x(1:dim-1);
   w=zeros(4,dim);           % initial coefficient vector
   d=zeros(dim,1);
   yl(1)=0; dl(1)=0;
   
   Sd=eye(4);
   for i=1:dim
      d(i)=-.6*dl(i)+x(i)+.01*x(i)*dl(i)+.02*xl(i)*dl(i)+n(i); % unknown system output sample
      uxl(:,i)=[x(i) dl(i) x(i)*dl(i) xl(i)*dl(i)]'; % new input vector
      elinha(i)=d(i)-w(:,i)'*uxl(:,i)+n(i);          % error sample
      psi=Sd*uxl(:,i);
      Sd=(1/lambda)*(Sd-(psi*psi')/(lambda+psi'*uxl(:,i)));
      w(:,i+1)=w(:,i)+elinha(i)*Sd*uxl(:,i);         % new coefficient vector
      y(i)=w(:,i+1)'*uxl(:,i);                       % output sample
      yl(i+1)=yl(i);
      dl(i+1)=d(i);
      e(i)=d(i)-y(i)+n(i);
   end
   mse(j,:)=e.^2;
end

MSE=mean(mse);
save ofile MSE;