基于Matlab实现神经网络GRNN算法（源码+数据）.rar

clear; clc; %网络训练样本 pConvert=importdata('C:\Users\Administrator\Desktop\ln\SelfOrganizationtrain.dat');; p=pConvert'; %训练样本的目标矩阵 t=importdata('C:\Users\Administrator\Desktop\ln\SelfOrganizationtarget.dat'); plot3(p(1,:),p(2,:),p(3,:),'o'); grid;box; for i=1:29,text(p(1,i),p(2,i),p(3,i),sprintf(' %g',t(i))),end hold off f=t; index1=find(f==1); index2=find(f==2); index3=find(f==3); index4=find(f==4); line(p(1,index1),p(2,index1),p(3,index1),'linestyle','none','marker','*','color','g'); line(p(1,index2),p(2,index2),p(3,index2),'linestyle','none','marker','*','color','r'); line(p(1,index3),p(2,index3),p(3,index3),'linestyle','none','marker','+','color','b'); line(p(1,index4),p(2,index4),p(3,index4),'linestyle','none','marker','+','color','y'); box;grid on;hold on; axis([0 3500 0 3500 0 3500]); title('训练用样本及其类别'); xlabel('A'); ylabel('B'); zlabel('C'); t=t'; t=ind2vec(t); spread=30; % PNN网络的创建和训练过程 net=newgrnn(p,t,spread); A=sim(net,p) Ac=vec2ind(A) plot3(p(1,:),p(2,:),p(3,:),'.'),grid;box; axis([0 3500 0 3500 0 3500]) for i=1:29,text(p(1,i),p(2,i),p(3,i),sprintf(' %g',Ac(i))),end %以图形方式输出训练结果 hold off f=Ac'; index1=find(f==1); index2=find(f==2); index3=find(f==3); index4=find(f==4); line(p(1,index1),p(2,index1),p(3,index1),'linestyle','none','marker','*','color','g'); line(p(1,index2),p(2,index2),p(3,index2),'linestyle','none','marker','*','color','r'); line(p(1,index3),p(2,index3),p(3,index3),'linestyle','none','marker','+','color','b'); line(p(1,index4),p(2,index4),p(3,index4),'linestyle','none','marker','+','color','y'); box;grid on;hold on; title('网络训练结果'); xlabel('A'); ylabel('B'); zlabel('C'); %对待分类样本进行分类 pConvert=importdata('C:\Users\Administrator\Desktop\ln\SelfOrganizationSimulation.dat'); p=pConvert'; a=sim(net,p) ac=vec2ind(a)