modeling.m

Least Mean Square Newton Algorithm

%
%	Modeling
%
%
% Last updated on April 28, 1998
%

itn=input('\n No. of iterations?      ');
sigman2=input('\n Variance of the plant noise?      ');
sigman=sqrt(sigman2);
wo=input('\n Plant impulse response (vector, w_o)?      ');
a=size(wo);
if a(1)<a(2)
   wo=wo';
end

N=input('\n Length of the model (N)?      ');

h=input('\n Coloring filter impulse response (vector, h)?     ');
a=size(h);
if a(1)<a(2)
   h=h';
end

Misad=input('\n Misadjustment (e.g., 0.1 for 10%) ?     ');
mu=Misad/(N*(h'*h));

a=input('\n Do you wish to see the values of \n      eigenvalue spread, expected MSE, ... (Y/N)?      ','s');
if (a=='y')|(a=='Y')
   MMSE=sigman2;
   R=corlnm2(h,N);
   lambda=eig(R);
   eignsprd=max(lambda)/min(lambda);
   taumax=1/(4*mu*min(lambda));
   taumin=1/(4*mu*max(lambda));
   MSEaprx=MMSE*(1+Misad);
   disp(' ')
   disp(' ')
   disp([' Eigenvalue spread = ' num2str(eignsprd)])
   disp([' Maximum time constant of the learning curve = ' num2str(taumax)])
   disp([' Minimum time constant of the learning curve = ' num2str(taumin)])
   disp([' Expected steady-state MSE = ' num2str(MSEaprx)])
end

runs=input('\n \n No. of runs (for ensemble averaging)? ');
xi=zeros(itn,1);

for k=1:runs
	x=filter(h,1,randn(itn,1));
	d=filter(wo,1,x)+sigman*randn(itn,1);
	w=zeros(N,1);

	for n=N:itn;
		xtdl=x(n:-1:n-N+1);
		e=d(n)-w'*xtdl;
		w=w+2*mu*e*xtdl;
		xi(n)=xi(n)+e^2;
	end
end
xi=xi/runs;


semilogy(xi)
xlabel('NO. OF ITERATIONS')
ylabel('MSE')