ShenJingWangLuo.rar_gupiaojing_神经网络原理

p=[58478 135185 5.46 0.23 16.5 0.21 1005.3 585.44; 67884 152369 5.46 0.27 18.7 0.26 1105.6 575.03; 74462 182563 6.01 0.25 21.6 0.28 1204.6 601.23; 78345 201587 6.12 0.26 25.8 0.29 1316.5 627.89; 82067 225689 6.21 0.26 30.5 0.31 1423.5 676.95; 89403 240568 6.37 0.28 34.9 0.33 1536.2 716.32; 95933 263856 6.38 0.28 39.8 0.36 1632.6 765.24; 104790 285697 6.65 0.3 42.5 0.39 1753.2 812.22; 116694 308765 6.65 0.3 46.7 0.41 1865.5 875.26]'; t=[102569 52365 46251; 124587 60821 56245; 148792 69253 67362; 162568 79856 78165; 186592 91658 90548; 205862 99635 98758; 226598 109862 102564; 245636 12056 111257; 263595 130378 120356]'; a=[1 2 3 5 7 8]; P=p; T=t; %归一化处理,数据小于1的不必处理 for i=1:6 P(a(i),:)=(p(a(i),:)-min(p(a(i),:)))/(max(p(a(i),:))-min(p(a(i),:))); end for i=1:3 T(i,:)=(t(i,:)-min(t(i,:)))/(max(t(i,:))-min(t(i,:))); end P_train=[P(:,1),P(:,2),P(:,3),P(:,4),P(:,5),P(:,6),P(:,7)]; T_train=[T(:,1),T(:,2),T(:,3),T(:,4),T(:,5),T(:,6),T(:,7)]; P_test=[P(:,8),P(:,9)]; T_test=[T(:,8),T(:,9)];%前7组数据做训练数据,后2组数据做测试数据 for i=1:5 net=newgrnn(P_train,T_train,i/10);%光滑因子分别为0.1到0.5 temp=sim(net,P_train);%网络对训练数据的逼近 j=3*i; y_out(j-2,:)=temp(1,:); y_out(j-1,:)=temp(2,:); y_out(j,:)=temp(3,:); temp=sim(net,P_test);%网络的预测输出 y(j-2,:)=temp(1,:); y(j-1,:)=temp(2,:); y(j,:)=temp(3,:); end y1=[y_out(1,:);y_out(2,:);y_out(3,:)]; y2=[y_out(4,:);y_out(5,:);y_out(6,:)]; y3=[y_out(7,:);y_out(8,:);y_out(9,:)]; y4=[y_out(10,:);y_out(11,:);y_out(12,:)]; y5=[y_out(13,:);y_out(14,:);y_out(15,:)]; y6=[y(1,:);y(2,:);y(3,:)]; y7=[y(4,:);y(5,:);y(6,:)]; y8=[y(7,:);y(8,:);y(9,:)]; y9=[y(10,:);y(11,:);y(12,:)]; y10=[y(13,:);y(14,:);y(15,:)]; %计算逼近误差 for i=1:7 error1(i)=norm(y1(:,1)-T_train(:,1)); error2(i)=norm(y2(:,1)-T_train(:,1)); error3(i)=norm(y3(:,1)-T_train(:,1)); error4(i)=norm(y4(:,1)-T_train(:,1)); error5(i)=norm(y5(:,1)-T_train(:,1)); end %计算预测误差 for i=1:2 error6(i)=norm(y6(:,i)-T_test(:,i)); error7(i)=norm(y7(:,i)-T_test(:,i)); error8(i)=norm(y8(:,i)-T_test(:,i)); error9(i)=norm(y9(:,i)-T_test(:,i)); error10(i)=norm(y10(:,i)-T_test(:,i)); end r=1:7; figure(1) plot(r,error1,'-*',r,error2,'-+',r,error3,'-h',r,error4,'-d',r,error5,'-o')%绘制逼近误差曲线 figure(2) r=1:2; plot(r,error6,'-*',r,error7,'-+',r,error8,'-h',r,error9,'-d',r,error10,'-o')%绘制预测误差曲线