LSSVM.zip

clear all; clc; load('MGS17.mat'); %% %训练和测试集以及参数 train_data = MGS17(:,1:5000); test_data = MGS17(:,4980:9979); time_step = 20; trnX = zeros(4980,20); trnY = zeros(4980,1); tstX = zeros(4980,20); tstY = zeros(4980,1); predictY = zeros(4980,1); C = 0.1; sigma = 0.2; %% %设置训练测试集输入输出 for i =1:length(train_data)-time_step trnX(i,:) = train_data(:,i:time_step+i-1); trnY(i,:) = train_data(time_step+i); tstX(i,:) = test_data(:,i:time_step+i-1); tstY(i,:) = test_data(time_step+i); end %% %迭代更新测试集输入 testX = trnX(end,:); [alpha,b,Cii,K0] = train(trnX,trnY,C,sigma); for i = 1:length(test_data)-time_step size = length(testX); Kt = rbf(testX,trnX,sigma); predictY(i,:) = Kt*alpha+b; if i<length(test_data)-time_step testX = [testX(2:end),predictY(i,:)]; %迭代更新 end end RMSe = sqrt(sum((tstY-predictY).^2)/length(predictY)); %% %绘图 figure(); xlabel('time') ylabel('MGS17') title('recurive_predict') plot(tstY,'b') hold on plot(predictY,'r') legend('真实值','预测值');