Generate 10 Normally distributed random vectors with minimum

Generate 10 Normally distributed random vectors with minimum mean square error and maximum scatter wing matlab.

Solution

x = zeros(10,1); d = x; % Initialize variables ha = fir1(31,0.5); % FIR system to be identified x = filter(sqrt(0.75),[1 -0.5],sign(randn(size(x)))); n = 0.1*randn(size(x)); % observation noise signal d = filter(ha,1,x)+n; % desired signal l = 32; % Filter length mu = 0.008; % LMS step size. m = 5; % Decimation factor for analysis % and simulation results ha = adaptfilt.lms(l,mu); [mmse,emse,meanW,mse,traceK] = msepred(ha,x,d,m); [simmse,meanWsim,Wsim,traceKsim] = msesim(ha,x,d,m); nn = m:m:size(x,1); subplot(2,1,1); plot(nn,meanWsim(:,12),\'b\',nn,meanW(:,12),\'r\',nn,... meanWsim(:,13:15),\'b\',nn,meanW(:,13:15),\'r\'); PlotTitle ={\'Average Coefficient Trajectories for\';... \'W(12), W(13), W(14), and W(15)\'}; title(PlotTitle); legend(\'Simulation\',\'Theory\'); xlabel(\'Time Index\'); ylabel(\'Coefficient Value\'); subplot(2,2,3); semilogy(nn,simmse,[0 size(x,1)],[(emse+mmse)... (emse+mmse)],nn,mse,[0 size(x,1)],[mmse mmse]); title(\'Mean-Square Error Performance\'); axis([0 size(x,1) 0.001 10]); legend(\'MSE (Sim.)\',\'Final MSE\',\'MSE\',\'Min. MSE\'); xlabel(\'Time Index\'); ylabel(\'Squared Error Value\'); subplot(2,2,4); semilogy(nn,traceKsim,nn,traceK,\'r\'); title(\'Sum-of-Squared Coefficient Errors\'); axis([0 size(x,1)... 0.0001 1]); legend(\'Simulation\',\'Theory\'); xlabel(\'Time Index\'); ylabel(\'Squared Error Value\');