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%lmsËã·¨Ô´³ÌÐò  
 
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);  
%add some noise  
n = n * std(y)/(10*std(n));  
d = y + n;  
totallength=size(d,1);  
%Take 60 points for training  
N=60 ;  
%begin 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])