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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); -d some noise

n = n * std(y)/(10*std(n)); d = y + n;

totallength=size(d,1);

%Take 60 points for training N=60 ;

?gin 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 Estimation Error in dB') ; figure ;

semilogy(w_err) ;

title('Weight Estimation Error') ; xlabel('Iteration Number') ;