【GRNN回归预测】基于GRNN实现流量回归预测附matlab代码.zip

%% 清空环境变量 clc; clear all close all nntwarn off; %% 载入数据 load data; % 载入数据并将数据分成训练和预测两类 p_train=p(1:12,:); t_train=t(1:12,:); p_test=p(13,:); t_test=t(13,:); %% 交叉验证 desired_spread=[]; mse_max=10e20; desired_input=[]; desired_output=[]; result_perfp=[]; indices = crossvalind('Kfold',length(p_train),4); h=waitbar(0,'正在寻找最优化参数....'); k=1; for i = 1:4 perfp=[]; disp(['以下为第',num2str(i),'次交叉验证结果']) test = (indices == i); train = ~test; p_cv_train=p_train(train,:); t_cv_train=t_train(train,:); p_cv_test=p_train(test,:); t_cv_test=t_train(test,:); p_cv_train=p_cv_train'; t_cv_train=t_cv_train'; p_cv_test= p_cv_test'; t_cv_test= t_cv_test'; [p_cv_train,minp,maxp,t_cv_train,mint,maxt]=premnmx(p_cv_train,t_cv_train); p_cv_test=tramnmx(p_cv_test,minp,maxp); for spread=0.1:0.1:2; net=newgrnn(p_cv_train,t_cv_train,spread); waitbar(k/80,h); disp(['当前spread值为', num2str(spread)]); test_Out=sim(net,p_cv_test); test_Out=postmnmx(test_Out,mint,maxt); error=t_cv_test-test_Out; disp(['当前网络的mse为',num2str(mse(error))]) perfp=[perfp mse(error)]; if mse(error)<mse_max mse_max=mse(error); desired_spread=spread; desired_input=p_cv_train; desired_output=t_cv_train; end k=k+1; end result_perfp(i,:)=perfp; end; close(h) disp(['最佳spread值为',num2str(desired_spread)]) disp(['此时最佳输入值为']) desired_input disp(['此时最佳输出值为']) desired_output %% 采用最佳方法建立GRNN网络 net=newgrnn(desired_input,desired_output,desired_spread); p_test=p_test'; p_test=tramnmx(p_test,minp,maxp); grnn_prediction_result=sim(net,p_test); grnn_prediction_result=postmnmx(grnn_prediction_result,mint,maxt); grnn_error=t_test-grnn_prediction_result'; disp(['GRNN神经网络三项流量预测的误差为',num2str(abs(grnn_error))]) save best desired_input desired_output p_test t_test grnn_error mint maxt figure plot(t_test,'ro-');hold on plot(grnn_prediction_result,'bs-');hold on xlabel('样本'); ylabel('数值') legend('真实值','GRNN')