基于径向基函数 （RBF） 神经网络的麦基格拉斯时间序列预测（Matlab代码实现）

%%  Results
% Input and output signals (training phase)
figure
plot(indt,f_train,'k','linewidth',lw);
hold on;
plot(indt,y_train,'.:b','linewidth',lw);
xlim([start_of_series_tr+time_steps end_of_series_tr]);
h=legend('Actual Value (Training)','RBF Predicted (Training)','Location','Best');
grid minor
xlabel('Sample #','FontSize',fsize);
ylabel('Magnitude','FontSize',fsize);
set(h,'FontSize',12)
set(gca,'FontSize',13)
saveas(gcf,strcat('Time_SeriesTraining.png'),'png')
% Input and output signals (test phase)
figure
plot(indts,f_test,'k','linewidth',lw);
hold on;
plot(indts,y_test,'.:b','linewidth',lw);
xlim([start_of_series_ts+time_steps end_of_series_ts]);
h=legend('Actual Value (Testing)','RBF Predicted (Testing)','Location','Best');
grid minor
xlabel('Sample #','FontSize',fsize);
ylabel('Magnitude','FontSize',fsize);
set(h,'FontSize',12)
set(gca,'FontSize',13)
saveas(gcf,strcat('Time_SeriesTesting.png'),'png')
% Objective function (MSE) (training phase)
figure
plot(start_of_series_tr:end_of_series_tr-1,10*log10(I(1:end_of_series_tr-start_of_series_tr)),'+-b','linewidth',lw)
h=legend('RBF (Training)','Location','North');
grid minor
xlabel('Sample #','FontSize',fsize);
ylabel('MSE (dB)','FontSize',fsize);
set(h,'FontSize',12)
set(gca,'FontSize',13)
saveas(gcf,strcat('Time_SeriesTrainingMSE.png'),'png')
% Objective function (MSE) (test phase)
figure
plot(start_of_series_ts+time_steps:end_of_series_ts,10*log10(I(end_of_series_tr-start_of_series_tr+1:end)),'.:b','linewidth',lw+1)
h=legend('RBF (Testing)','Location','South');
grid minor
xlabel('Sample #','FontSize',fsize);
ylabel('MSE (dB)','FontSize',fsize);
set(h,'FontSize',12)
set(gca,'FontSize',13)
saveas(gcf,strcat('Time_SeriesTestingMSE.png'),'png')
% Mean square error
10*log10(((f_train'-y_train)*(f_train'-y_train)')/length(y_train))
10*log10(((f_test'-y_test)*(f_test'-y_test)')/length(y_test))