[交流] 【转帖】（分形）布朗运动的Matlab生成代码 已有3人参与

function similar_sequence = generator_FFT(n,H)   
%--------------------------------------------------------------------------  
% GENERATOR_FFT Use fast fourier transform to generate normalized FGN   
%     and FBM. Then use Norrs method to generate normalized  
%     similar_sequence. Finally, the average of similar_sequence was set to   
%     1 through normaliztion. 
%     
%     Note:   
%     1. The input argument n is the number of point of sequence. It must |
   
%     be even. H is the objective similarity you want. 
%     2. The output argument similar_sequence is a similar_sequence with 
%     average equal to 1. The FGN and FBM are normalized FGN and FBM   
%     respectively.   
%     3. This routine is a matlab version of paxson's R routine. For more   
%     details, see "Fast, approximate synthesis of fractional Gaussian   
%     noise for generating self-similar network traffic
%--------------------------------------------------------------------------  

%--------------------------------------------------------------------------   
%  
%    generator_FFT   
%    Edit by Chu Chen, 07/07/2007
%    Should you have any suggestion for improving the code, please contact:  
%    [email]chuch@scut.edu.cn[/email].
%--------------------------------------------------------------------------

if mod(n,2) ~= 0 
    error('The input argument "n" must be even');
else   
    % Returns a Fourier-generated sample path of a "self similar" process   
    % Consisting of n points(n should be even) and Hurst paramenter H   
    n = n/2;   
    lambda = [1:n]*pi/n;  

    % Approxiamte ideal power spectrum.  
    f = FGNspectrum(lambda,H);
  
    % Adjust for estimating power spectrum via periodogram   
    f = f.*exprnd(1,1,n); 
  
    % Construct corresponding complex numbers with randm phase  
    alpha = 2*pi.*unifrnd(0,1,1,n);
    a = sqrt(f).*cos(alpha);   
    b = sqrt(f).*sin(alpha);
    z = complex(a,b); 
  
    % Last element should have zero phase
    z(n) = abs(z(n));
  
    % Expand z to correspond to a Fourier transform of a real-valued signal.   
    zprime = [0,z,conj(fliplr(z(1:n-1)))]; 
  
    % Inverse FFT gives sample path.
    FGN = real(ifft(zprime));   
      
    % Standardize FGN and create FBM.   
    FGN = (FGN-mean(FGN))/std(FGN);
    FBM = cumsum(FGN);   

    % Use Norrs method to generate normalized similar_sequence
    similar_sequence = FGN;   
      
    % M = 30;   
    % a = 5;   
    % similar_sequence = M + sqrt(a*M)*similar_sequence;   
    % similar_sequence = max(0,similar_sequence);  
    % similar_sequence = similar_sequence*2*n/sum(similar_sequence);   
end;   
%----------------------------subfunction1----------------------------------  
function f = FGNspectrum(lambda,H) 
% Returns an approximation of the power spectrum of FGN at the given   
% frequencies lambda and the given Hurst parameter H.   
f = 2*sin(pi*H)*gamma(2*H+1).*(1-cos(lambda)).*(lambda.^(-2*H-1) + FGNest(lambda,H));  
  
%----------------------------subfunction2----------------------------------  
function est = FGNest(lambda,H)  
% Returns the estimate for B(lambda,H).  
d = -2*H-1;   
dprime = -2*H;   
a1 = 2*1*pi+lambda;   
b1 = 2*1*pi-lambda;   
a2 = 2*2*pi+lambda; `  
b2 = 2*2*pi-lambda;   
a3 = 2*3*pi+lambda;   
b3 = 2*3*pi-lambda;  
a4 = 2*4*pi+lambda;   
b4 = 2*4*pi-lambda;  
est = a1.^d + b1.^d + a2.^d + b2.^d + a3.^d + b3.^d + (a3.^dprime+b3.^dprime+a4.^dprime+b4.^dprime)/(8*pi*H)