[人工智能] 有没有神经网络实例，可以学习一下怎么编程？

clear%数据输入huanghe_p=[370503434575490420560640558343326405446423422697598377435472451667601689541485425389382707422];huanghe_t=[5157135867537205677179878104894535896395685959828495196156525999418939997587016305615201040 535];%归一化处理p=(huanghe_p-min(huanghe_p))/(max(huanghe_p)-min(huanghe_p));t=(huanghe_t-min(huanghe_t))/(max(huanghe_t)-min(huanghe_t));%数据输入2：网络有关参数EPOCHS=10000;GOAL=0.000005;%建立bp神经网络，并训练，仿真。其中输入为p，输出为t%-------------------------隐层神经元确定-----------------------------s=3:15;%s 为常向量，表示神经元的个数res=zeros(size(s));%res将要存储误差向量，这里先置零pn=[p(1:5);p(6:10);p(11:15);p(16:20)];tn=[t(1:5);t(6:10);t(11:15);t(16:20)];for i=1:length(s)               net=newff(minmax(pn),[s(i),4],{'tansig','purelin'},'trainlm');    net.iw{1,1}=zeros(size(net.iw{1,1}))+0.5;    net.lw{2,1}=zeros(size(net.lw{2,1}))+0.75;    net.b{1,1}=zeros(size(net.b{1,1}))+0.5;    net.b{2,1}=zeros(size(net.b{2,1}));    net.trainParam.epochs=EPOCHS;    net.trainParam.goal =GOAL;    net=train(net,pn,tn);    y=sim(net,pn);e=tn-y;error=mse(e,net);res(i)=norm(error);end%选取最优神经元数，number为使得误差最小的隐层神经元个数number=find(res==min(res));if(length(number)>1) no=number(1)else no=numberendclear error，res%选定隐层神经元数目后，建立网络，训练仿真。net=newff(minmax(pn),[no,4],{'tansig','purelin'},'trainlm');    net.iw{1,1}=zeros(size(net.iw{1,1}))+0.5;    net.lw{2,1}=zeros(size(net.lw{2,1}))+0.75;    net.b{1,1}=zeros(size(net.b{1,1}))+0.5;    net.b{2,1}=zeros(size(net.b{2,1}));    net.trainParam.epochs=EPOCHS;    net.trainParam.goal =GOAL;net=train(net,pn,tn);y=sim(net,pn);e=tn-y;error=mse(e,net)%error为网络的误差向量r=norm(error);%r为网络的整体误差save net                      %保存最好的网络%预测input=[p(11:15);p(16:20);p(21:25);p(26:30)];yuce=sim(net,input);%结果反归一化y1=[y(1, y(2, y(3, y(4,];yuce1=[yuce(1, yuce(2, yuce(3, yuce(4,];t1=y1*(max(huanghe_t(1:20))-min(huanghe_t(1:20)))+min(huanghe_t(1:20));yuce2=yuce1*(max(huanghe_t(1:20))-min(huanghe_t(1:20)))+min(huanghe_t(1:20));%计算误差wucha=abs(t1-huanghe_t(1:20))./huanghe_t(1:20)b=minmax(wucha);average_wucha=mean(wucha);%作图figure(1)plot(1:20,huanghe_t(1:20),'*-',1:20,t1,'o:')figure(2)plot(1:20,huanghe_t(11:30),'*-',1:20,yuce2,'o:')