www.pudn.com > adaptivefiltering.rar > run_rls_eq.m

function rsleq(rp) 
 
% Computer Experiment 
% Section 13.7, Adaptive Filter Theory, 3rd edition 
% Adaptive equalization 
 
seed  = 0:(rp.Niter-1); 
h      = [1/2*(1+cos(2*pi/rp.T*((1:3) - 2)))]; 
 
Npred = rp.Ndata - 1 - rp.p; 
A = zeros(rp.Niter, Npred); 
E = zeros(rp.Niter, Npred); 
Wx = zeros(rp.p, Npred); 
for iter = 1:rp.Niter, 
  disp([rp.T iter]) 
  rand('seed', seed(iter)); 
  randn('seed', seed(iter)); 
  a = [2*(rand(rp.Ndata, 1) > 0.5) - 1]; 
  Xi = conv(a, h); 
 
  % account for shift in input sequence introduced by 
  % convolution with h and offset 1 addressing in MATLAB 
  Xi = Xi((length(h)+1):length(Xi)); 
keyboard 
  Xi = Xi + sqrt(rp.var_v)*randn(length(Xi), 1); 
  [Xi, Y] = mksstrndata(Xi, rp.p); 
  Y = a((rp.tau+1):rp.Ndata); 
%  keyboard 
  [Wo, xp, alpha, e] = rls_AR_pred(Xi, Y, rp.verbose, rp.lambda, rp.delta); 
  A(iter, :) = alpha; 
  E(iter, :) = e; 
  Wx = Wx + Wo; 
end; % for iter 
Wx = Wx / rp.Niter; 
 
eval(['save ', rp.name])