lms.m

仿真AR（2）模型的LMS自适应滤波器。

clear all;
u=0.04;
n=2000;
a1=-0.2;
a2=1;
a3=[1 a1 a2];
b1=1;
m1=randn(1,n); 
m1=m1/std(m1); 
m1=m1-mean(m1); 
a=0; 
b=sqrt(0.0965); 
m1=a+b*m1; 
[x zf]=filter(b1,a3,m1); 
w1(1)=0;w2(1)=0;
e(1)=x(1);e(2)=x(2)/(1+(2*u*x(1)*x(1)));%e(2)=x(2)-w1(2)*x(1);
w1(2)=2*u*e(2)*x(1);
w2(2)=0;
for i=3:n
    e(i)=(x(i)-(x(i-1)*w1(i-1))-(x(i-2)*w2(i-1)))/(1+(2*u*x(i-1)*x(i-1))+(2*u*x(i-2)*x(i-2)));
    w1(i)=w1(i-1)+2*u*e(i)*x(i-1);
    w2(i)=w2(i-1)+2*u*e(i)*x(i-2);
end
m=1:n;
figure(1);
plot(m,w1,':',m,w2);
title('Weight Estimation ') ;
xlabel('Iteration Number') ;
ylabel('Weight') ;
legend('W1 Estimation','W2 Estimation')
figure(2);
plot(m,abs(e));
title('Learning Curve') ;
xlabel('Iteration Number') ;
ylabel('Output Estimation Error') ;