matlab LMS自适应滤波的程序

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % LMS自适应滤波的演示程序 % 参考的系统如下： % 实际输入信号x(n)=2^0.5*sin(0.05*pi*n)+v(n), % v(i)是均值为零，方差为1的高斯噪声,实际输入信号的信噪比是1 % 参考信号直接设为d=s; % 收敛因子u=0.0005 % 滤波器的长度为20点，即w的长度是20点 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% randn('seed', 0) ; rand('seed', 0) ; NoOfData = 8000 ; % Set no of data points used for training Order = 32 ; % Set the adaptive filter order Mu = 0.01 ; % 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 LMS w = zeros(Order,1) ; % LMS 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 ; 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') ;