自適應(yīng)的MATLAB的一種仿真.docVIP

clear all close all %channel system order sysorder = 5 ; % Number of system points N=2000; inp = randn(N,1); n = randn(N,1); b,a = butter(2,0.25); Gz = tf(b,a,-1); %This function is submitted to make inverse Z-transform (Matlab central file exchange) %The first sysorder weight value %h=ldiv(b,a,sysorder); % if you use ldiv this will give h :filter weights to be h= 0.0976; 0.2873; 0.3360; 0.2210; 0.0964; y = lsim(Gz,inp); %add some noise n = n * std(y)/(10*std(n); d = y + n; totallength=size(d,1); %Take 60 points for training N=60 ; %begin of algorithm w = zeros ( sysorder , 1 ) ; for n = sysorder : N u = inp(n:-1:n-sysorder+1) ; y(n)= w * u; e(n) = d(n) - y(n) ; % Start with big mu for speeding the convergence then slow down to reach the correct weights if n 20 mu=0.32; else mu=0.15; end w = w + mu * u * e(n) ; end %check of results for n = N+1 : totallength u = inp(n:-1:n-sysorder+1) ; y(n) = w * u ; e(n) = d(n) - y(n) ; end hold on plot(d) plot(y,r); title(System output) ; xlabel(Samples) ylabel(True and estimated output) figure semilogy(abs(e) ; title(Error curve) ; xlabel(Samples) ylabel(Error value) figure plot(h, k+) hold on plot(w, r*) legend(Actual weights,Estimated weights) title(Comparison of the actual weights and the estimated weights) ; axis(0 6 0.05 0.35) % RLS 算法 randn(seed, 0) ; rand(seed, 0) ; NoOfData = 8000 ; % Set no of data points used for training Order = 32 ; % Set the adaptive filter order Lambda = 0.98 ; % Set the forgetting factor Delta = 0.001 ; % R initialized to Delta*I 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 RLS P = Delta * eye ( Order, Order ) ; w = zeros ( Order, 1 ) ; % RLS Adaptation for n = Order : NoOfData ; u = x(n:-1:n-Order+1) ; pi_ = u * P ; k = Lambda + pi_ * u ; K = pi_/k; e(n) = d(n) - w * u ; w = w + K * e(n) ; PPrime = K * pi_ ; P = ( P - PPrime ) / Lambda ; w_err(n) = norm(h - w) ; end ; % Plot results figure ; plot(20*log10(abs(e) ; title(Learning Curve) ; xlabel(Iteration Number) ; ylabel(Output Es