对比NLMS,FxLMS以及FxNLMS三种自适应滤波matlab仿真+仿真录像

clc; clear; close all; warning off; addpath(genpath(pwd)); mus = [0.01 0.05 0.1 0.5 1 2 2.5 3];% 0.5 1]; for j = 1:length(mus) for i = 1:100 %%INITIALIZE VALUES%% % generate input signal muOG=mus(j); %learning rate M=128; %buffer size (num filter weights) x=randn(10000,1); %input signal x=x/max(x); %sample rate fs=8000; %number of samples of the input signal N=length(x); %length of input signal % generate known filter coefficients Pz=0.5*[0:127]; %linear coefficients %Pz=randn(128,1); %random coefficients ylim = max(Pz)*1.20; ymin = min(Pz)-.2*max(Pz); % generate filtered input signal == desired signal d=conv(Pz,x); %input signal filtered by known filter Pz (primary path) Sz = Pz/2; %run known signal through filter xp=conv(Sz,x); %initalize Sz hat values Szh=zeros(M,1); for n=M:N xpvec=x(n:-1:n-M+1); %input has to be in reverse orxer has to be %update mu mu(n) = 1/(xpvec'*xpvec); e(n)=xp(n)-Szh'*xpvec; %update error %plot(e) Szh=Szh+mu(n)*xpvec*(e(n)); %update filter coefficient end Szh = abs(ifft(1./abs(fft(Szh)))); emean=zeros(N,1); %filter input by learned filter to get x prime xph = conv(Szh,x); %initalize Wz filter values Wz=zeros(M,1); %Make sure that x and d are column vectors x=x(:); d=d(:); %LMS for n=M:N xvec=x(n:-1:n-M+1); %input has to be in reverse orxer xpvec=xph(n:-1:n-M+1); %update mu mu(n) = muOG/(xvec'*xvec); e(n)=d(n)-Wz'*xvec; %update error %plot(e) Wz=Wz+mu(n)*xpvec*(e(n)); %update filter coefficient end e=e(:); emean = (emean(:)+e); end emean=(emean)/i; if max(emean)>1e4 for l = 1:length(emean) if abs(emean(l)) > 1e3 emean(l)=1e3; end end eall(j,:)=emean; else try [eall(j,:),q]=(envelope(abs(emean),500,'peaks')); catch eall(j,:) = 1e3; end end end figure for i = 1:length(mus) plot(10*log10(abs(eall(i,:)))) hold on end title('Convergence Time in Cycles') ylabel('Error (dB)'); xlabel('Cycles'); legend('0.01','0.05','0.1','0.5','1.0', '2.0');