www.pudn.com > LMSandRLS.rar > RLS.m

% RLS ثم·¨  
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') ;