% 混沌時間序列的 volterra 預測(多步預測) -- 主函數% 使用平臺 - Matlab6.5 / Matlab7.0% 作者：陸振波，海軍工程大學% 歡迎同行來信交流與合作，更多文章與程序下載請訪問我的個人主頁% 電子郵件：luzhenbo@yahoo.com.cn% 個人主頁：http://luzhenbo.88uu.com.cnclcclearclose all%---------------------------------------------------% 產生混沌序列sigma = 10;             % Lorenz 方程參數 ab = 8/3;                %                 br = 34;                 %                 c            y = [-1,0,1];           % 起始點 (1 x 3 的行向量)h = 0.01;               % 積分時間步長k1 = 6000;              % 前面的迭代點數k2 = 5000;              % 后面的迭代點數 (總樣本數)z = LorenzData(y,h,k1+k2,sigma,r,b);X = z(k1+1:end,1);X = normalize_a(X,1);      % 信號歸一化到均值為0,振幅為1%----------------------------------------------------train_num = 500;       % 訓練樣本數test_num = 1000;       % 測試樣本數%----------------------------------------------------% 混沌序列的相空間重構 (phase space reconstruction)tau = 10m = 3p = 3X = X(1:train_num+test_num);[xn_train,dn_train] = PhaSpaRecon(X(1:train_num),tau,m);[xn_test,dn_test] = PhaSpaRecon(X(train_num+1:train_num+test_num),tau,m);%----------------------------------------------------[Wn,err_mse1] = volterra_train_lu(xn_train,dn_train,p);err_mse1 = err_mse1/var(X)%----------------------------------------------------% 多步預測len_pred = 300;x_start = X(train_num-(m-1)*tau:train_num);dn_pred = zeros(len_pred,1);for i=1:len_pred    xn_start = PhaSpaRecon(x_start,tau,m);    dn_pred(i) = volterra_test(xn_start,p,Wn);    x_start = [x_start(2:end);dn_pred(i)];enddn_test = X(train_num+1:train_num+len_pred);%----------------------------------------------------% 作圖plot(train_num+1:train_num+len_pred,dn_test,'r',...     train_num+1:train_num+len_pred,dn_pred,'b');legend('真實值','預測值',0);