lms1.m

本源码用MATLAB语言实现了自适应LMS算法

randn('seed', 0) ;
rand('seed', 0) ;

NoOfData = 1785 ;  % Set no of data points used for training
Order = 40 ;    % Set the adaptive filter order

Mu = 0.01 ;    % Set the step-size constant

x = randn(NoOfData, 1) ;% Input assumed to be white
t=0:((4*pi)/1784):4*pi;
x=x+(sin(t))';
h = rand(Order, 1) ;  % System picked randomly
% d=sin(t);
d = filter(h, 1, x) ;  % Generate output (desired signal)

% Initialize LMS

w = zeros(Order,1) ;

% LMS Adaptation

for n = Order : NoOfData
  D = x(n:-1:n-Order+1) ;
  e(n) = d(n) - w'*D ;
  w = w + Mu*e(n)*D ;
  y(n)=w'*D;
  w_err(n) = norm(h - w) ;
end ;
e
xn=fft(x)
yn=fft(y)
% Plot results
figure;
subplot(211),plot(x);
subplot(212),plot(y);

figure;
subplot(211),plot(x);
subplot(212),plot(e);
figure
subplot(211);
plot(abs(xn));
ylabel('输入');
subplot(212);
plot(abs(yn));
ylabel('输出');
title('FFT频谱分析图')

% 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') ;