nlms_demo.m

数字信号处理基础与应用 张延华著

%  Normalized LMS Algorithm 
randn('seed', 0) ;
rand('seed', 0) ;

NoOfData = 8000 ;	% Set no of data points used for training
Order = 32 ;		% Set the adaptive filter order

Mu = 1.0 ;		% Set the step-size constant

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 NLMS

w = zeros(Order,1) ;

% NLMS Adaptation

for n = Order : NoOfData
	D = x(n:-1:n-Order+1) ;
	d_hat(n) = w'*D ;
	e(n) = d(n) - d_hat(n) ;
	w = w + Mu*e(n)*D/(D'*D) ;
	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') ;