% RLS 算法
randn('seed', 0) ;
rand('seed', 0) ;

NoOfData = 8000 ;  % Set no of data points used for training
Order = 32 ;    % Set the adaptive filter order

Lambda = 0.98 ;    % Set the forgetting factor
Delta = 0.001 ;    % R initialized to Delta*I

x = randn(NoOfData, 1) ;% Input assumed to be white
h = rand(Order, 1) ;  % System picked randomly
d = filter(h, 1, x) ;  % Generate output (desired signal)

% Initialize RLS

P = Delta * eye ( Order, Order ) ;
w = zeros ( Order, 1 ) ;

% RLS Adaptation

for n = Order : NoOfData ;
    
  u = x(n:-1:n-Order+1) ;
  pi_ = u' * P ;
  k = Lambda + pi_ * u ;
  K = pi_'/k;
  e(n) = d(n) - w' * u ;
  w = w + K * e(n) ;
  PPrime = K * pi_ ;
  P = ( P - PPrime ) / Lambda ;
  w_err(n) = norm(h - w) ;

end ;

% Plot results

figure ;
plot(20*log10(abs(e))) ;
title('Learning Curve') ;
xlabel('Iteration Number') ;
ylabel('Output Estimation Error in dB') ;

figure ;
semilogy(w_err) ;
title('Weight Estimation Error') ;
xlabel('Iteration Number') ;
ylabel('Weight Error in dB') ;