% 主文件%clcclearclose all%---------------------------------------------------% 產生 Lorenz 時間序列sigma = 16;          % Lorenz 方程參數 ab = 4;               %                 br = 45.92;           %                 c            y = [1,0,0];        % 起始點 (1 x 3 的行向量)h = 0.01;            % 積分時間步長k1 = 2000;           % 前面的迭代點數k2 = 3000;           % 后面的迭代點數z = LorenzData(y,h,k1+k2,sigma,r,b);z = z(k1+1:end,:);X = z(:,1);X = normalize_1(X);  % 歸一化到均值為 0，振幅為 1%========================================================    maxLags = 1;m_vector = 2:5;sigma = std(X);r_vector = sigma/2*[1:4];S_mean = zeros(1,maxLags);Sj = zeros(1,length(r_vector));delta_S_mean = zeros(1,maxLags);delta_S = zeros(length(m_vector),maxLags);ticfor t = 1:maxLags    t    temp = 0;    for i = 1:length(m_vector)        for j = 1:length(r_vector)            m = m_vector(i);            r = r_vector(j);            S = ccFunction(m,X,r,t);         % 文獻中的標準算法            temp = temp + S;            Sj(j) = S;        end        delta_S(i,t) = max(Sj)-min(Sj);    end    % 參見 <<混沌時間序列分析及應用>> P69 式(3.31)    S_mean(t) = temp/(length(m_vector)*length(r_vector));    delta_S_mean = mean(delta_S);endS_cor = delta_S_mean + abs(S_mean);toc%-----------------------------------------------------------------------figure(1)    subplot(311)plot(1:maxLags,S_mean);grid;subplot(312)plot(1:maxLags,delta_S_mean);grid;subplot(313)plot(1:maxLags,S_cor);grid;