%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 errorclear all		% clear memoryload ifile;		% read input variablesfor 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;endMSE=mean(mse);save ofile MSE;