% GP算法求關(guān)聯(lián)維和嵌入維%clcclearclose allIsPlot = 1;%---------------------------------------------------% 產(chǎn)生 Lorenz 時(shí)間序列sigma = 10;          % Lorenz方程參數(shù)r = 28;               b = 8/3;          y = [-1;0;1];        % 起始點(diǎn) (3x1 的列向量)h = 0.01;            % 積分時(shí)間步長(zhǎng)k1 = 30000;           % 前面的迭代點(diǎn)數(shù)k2 = 3000;           % 后面的迭代點(diǎn)數(shù)z = LorenzData(y,h,k1+k2,sigma,r,b);X = z(k1+1:end,1);X = normalize_1(X);  % 歸一化到均值為 0，振幅為 1%---------------------------------------------------'disp('----- GP算法求關(guān)聯(lián)維和嵌入維 -----');t = 10;m_vector = 2:5;r_vector = exp(-5:0.5:0);num_m = length(m_vector);num_r = length(r_vector);ln_Cr = zeros(num_m,num_r);%------------------------------------------------------tictype_norm = 2       % 使用范數(shù)類型 (缺省值為2)                    % type_norm = 0,1,2時(shí)，分別對(duì)應(yīng)無(wú)窮范數(shù)、1范數(shù)和2范數(shù)block = 1           % 分塊計(jì)計(jì)算關(guān)聯(lián)積分 - 分塊數(shù) (缺省值為1)                    % t越大速度越快，但有誤差for i = 1:num_m    for j = 1:num_r        % 計(jì)算關(guān)聯(lián)積分S(m,N,r,t), 參見(jiàn) <<混沌時(shí)間序列分析及應(yīng)用>> P35 式(2.29)        m = m_vector(i);        r = r_vector(j);        %ln_Cr(i,j) = log(CorrelationIntegral(m,X,r,t)); % 缺省用法        ln_Cr(i,j) = log(CorrelationIntegral(m,X,r,t,type_norm,block));     endendt = tocln_r = log(r_vector);plot(ln_r,ln_Cr','+:');grid;xlabel('ln(r)'); ylabel('ln(C(r))');title(['norm = ',num2str(type_norm),', block = ',num2str(block),', t = ',num2str(t)]);legend('m=2','m=3','m=4','m=5',4)