jens
b31f3e7435
git-svn-id: http://moon:8086/svn/matlab/trunk@153 801c6759-fa7c-4059-a304-17956f83a07c
61 lines
1.6 KiB
Matlab
Executable File
61 lines
1.6 KiB
Matlab
Executable File
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% %
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% RLS Algorithm %
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% %
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% Written By: Sundar Sankaran and A. A. (Louis) Beex %
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% DSP Research Laboratory %
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% Dept. of Electrical and Comp. Engg %
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% Virginia Tech %
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% Blacksburg VA 24061-0111 %
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% %
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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randn('seed', 0) ;
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rand('seed', 0) ;
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NoOfData = 8000 ; % Set no of data points used for training
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Order = 32 ; % Set the adaptive filter order
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Lambda = 0.98 ; % Set the forgetting factor
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Delta = 0.001 ; % R initialized to Delta*I
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x = randn(NoOfData, 1) ;% Input assumed to be white
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h = rand(Order, 1) ; % System picked randomly
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d = filter(h, 1, x) ; % Generate output (desired signal)
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% Initialize RLS
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P = Delta * eye ( Order, Order ) ;
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w = zeros ( Order, 1 ) ;
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% RLS Adaptation
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for n = Order : NoOfData ;
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u = x(n:-1:n-Order+1) ;
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pi_ = u' * P ;
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k = Lambda + pi_ * u ;
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K = pi_'/k;
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e(n) = d(n) - w' * u ;
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w = w + K * e(n) ;
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PPrime = K * pi_ ;
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P = ( P - PPrime ) / Lambda ;
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w_err(n) = norm(h - w) ;
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end ;
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% Plot results
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figure ;
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plot(20*log10(abs(e))) ;
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title('Learning Curve') ;
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xlabel('Iteration Number') ;
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ylabel('Output Estimation Error in dB') ;
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figure ;
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semilogy(w_err) ;
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title('Weight Estimation Error') ;
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xlabel('Iteration Number') ;
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ylabel('Weight Error in dB') ;
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