GRNN的数据预测-基于广义回归神经网络.doc

%% 案例8：GRNN的数据预测—基于广义回归神经网络的货运量预测 %% 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