?? main_volterra_multisteppred.m
字號:
% 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);
?? 快捷鍵說明
復制代碼
Ctrl + C
搜索代碼
Ctrl + F
全屏模式
F11
切換主題
Ctrl + Shift + D
顯示快捷鍵
?
增大字號
Ctrl + =
減小字號
Ctrl + -