vlms.m

来自「一个matlab实现IIR、FIR、LMS、NLMS等算法的m代码」· M 代码 · 共 40 行

%VLMS Volterra LMS algorithm
%
%   'ifile.mat' - input file containing:
%      Nr - members of ensemble
%      dim - iterations
%      Sx - standard deviation of input
%      Sn - standard deviation of measurement noise
%      u - convergence factor
%      Nw - length of the adaptive filter
%      
%   '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
   xl1=zeros(dim,1); xl2=xl1; 
   xl1(2:dim)=x(1:dim-1);    % x(k-1)
   xl2(3:dim)=x(1:dim-2);    % x(k-2)
   d=zeros(dim,1);
   d=-.76*x-xl1+xl2+.5*x.^2+2*x.*xl2-1.6*xl1.^2+1.2*xl2.^2+.8*xl1.*xl2+n; ...
     % unknown system output
   w=zeros(Nw,dim);           % initial coefficient vector
   uxl=[x xl1 xl2 x.^2 x.*xl1 x.*xl2 xl1.^2 xl1.*xl2 xl2.^2]'; % input vectors
   
   for i=1:dim
      e(i)=d(i)-w(:,i)'*uxl(:,i)+n(i);    % error sample
      y(i)=w(:,i)'*uxl(:,i);              % output sample
      w(:,i+1)=w(:,i)+2*u*e(i)*uxl(:,i);  % new coefficient vector
   end
   mse(j,:)=e.^2;
end 

MSE=mean(mse);
save ofile MSE;