1、GRNN的网络结构
2、代码例子：% 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 allclose allnntwarn 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_inputdisp(['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
3、与BP神经网络的比较：% GRNN的数据预测—基于广义回归神经网络的货运量预测%% 以下程序为案例扩展里的GRNN和BP比较 需要load chapter8.1的相关数据clear allload bestn=13p=desired_inputt=desired_outputnet_bp=newff(minmax(p),[n,3],{'tansig','purelin'},'trainlm');% 训练网络net.trainParam.show=50;net.trainParam.epochs=2000;net.trainParam.goal=1e-3;%调用TRAINLM算法训练BP网络net_bp=train(net_bp,p,t);bp_prediction_result=sim(net_bp,p_test);bp_prediction_result=postmnmx(bp_prediction_result,mint,maxt);bp_error=t_test-bp_prediction_result';disp(['BP神经网络三项流量预测的误差为',num2str(abs(bp_error))])
