LMS.zip_least mean square_least square_mean square error

close all %Producing the periodic signal t=0:0.001:2; A=10; xs=A*sin(2*pi*t); figure; subplot(2,1,1); plot(t,xs); grid; ylabel('amplitude'); title('original signal'); %Prodecing noise xn=randn(1,2001); subplot(212); plot(t,xn,'k'); xlabel('time'); ylabel('amplitude'); title(' randrom noise'); grid; %-------------------------------filter signal-----------------------------% xn = xs+xn; xn = xn.'; % input signal dn = xs.'; % expected signal M = 20 ; % filter order rho_max = max(eig(xn*xn.')); % the eigenvalue of maximum of input signal correlation matrix mu = rand()*(1/rho_max) ; % convergence factor 0 < mu < 1/rho [yn,W,en] = Lms(xn,dn,M,mu); %----------------Drawing the input signal of adaptive filter--------------% figure; subplot(311) plot(t,xs,'r'); title('sending signal'); axis([0,2,-A-1,A+1]); hold on grid on subplot(312); plot(t,xn,'k'); axis([0,2,-A-3,A+3]); hold on grid; ylabel('amplitude'); xlabel('time'); title('the input signal of adaptive filter'); subplot(3,1,3); plot(t,yn,'b'); axis([0,2,-A-1,A+1]); hold on grid; ylabel('amplitude'); xlabel('time'); title('output signal of adaptive filter'); %-------------Drawing output signal of adaptive filter, the expected output signal and their error-----------% figure plot(t,yn,'r',t,dn,'k',t,dn-yn,'b');grid; legend('the output signal of adaptive filter','expected output','error'); ylabel('amplitude'); xlabel('time'); title('adaptive filter');