clear all close all
%channel system order sysorder = 5 ;
% Number of system points N=2000;
inp = randn(N,1); n = randn(N,1);
[b,a] = butter(2,0.25); Gz = tf(b,a,-1);
%This function is submitted to make inverse Z-transform (Matlab central file exchange) %The first sysorder weight value %h=ldiv(b,a,sysorder)';
% if you use ldiv this will give h :filter weights to be h= [0.0976; 0.2873; 0.3360; 0.2210; 0.0964;];
y = lsim(Gz,inp); -d some noise
n = n * std(y)/(10*std(n)); d = y + n;
totallength=size(d,1);
%Take 60 points for training N=60 ;
?gin of algorithm
w = zeros ( sysorder , 1 ) ; for n = sysorder : N
u = inp(n:-1:n-sysorder+1) ; y(n)= w' * u;
e(n) = d(n) - y(n) ;
% Start with big mu for speeding the convergence then slow down to reach the correct weights if n < 20 mu=0.32; else
mu=0.15; end
w = w + mu * u * e(n) ; end
%check of results
for n = N+1 : totallength
u = inp(n:-1:n-sysorder+1) ; y(n) = w' * u ; e(n) = d(n) - y(n) ; end hold on plot(d) plot(y,'r');
title('System output') ; xlabel('Samples')
ylabel('True and estimated output') figure
semilogy((abs(e))) ; title('Error curve') ; xlabel('Samples') ylabel('Error value') figure
plot(h, 'k+') hold on plot(w, 'r*')
legend('Actual weights','Estimated weights')
title('Comparison of the actual weights and the estimated weights') ; axis([0 6 0.05 0.35])
% 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') ;