9-5.m

神经网络与matlab7的实现 配套资料下载 .m格式

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	0.30	42.5	0.39	1753.2	812.22;
116694	308765	6.65	0.30	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	120566	111257;
263595	130378	120356]';
%设定需要归一化的行序号
a=[1 2 3 5 7 8];
P=p;
%对输入向量进行归一化处理
for i=1:6
    P(a(i),:)=(p(a(i),:)-min(p(a(i),:)))/(max(p(a(i),:))-min(p(a(i),:)));
end
%对目标向量进行归一化处理
for i=1:3
    T(i,:)=(t(i,:)-min(t(i,:)))/(max(t(i,:))-min(t(i,:)));
end
%训练样本，P_train为输入向量，T_train为目标向量
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)];
%测试样本，P_test为输入向量，T_test为目标向量
P_test=[P(:,8) P(:,9)];
T_test=[T(:,8) T(:,9)];
for i=1:5
    net=newgrnn(P_train,T_train,i/10);
    %网络对训练数据的逼近
    temp=sim(net,P_train);
    j=3*i;
    y_out(j-2,:)=temp(1,:);
    y_out(j-1,:)=temp(2,:);
    y_out(j,:)=temp(3,:);
    %网络的预测输出
    temp=sim(net,P_test);
    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(:,i)-T_train(:,i));
    error2(i)=norm(y2(:,i)-T_train(:,i));
    error3(i)=norm(y3(:,i)-T_train(:,i));
    error4(i)=norm(y4(:,i)-T_train(:,i));
    error5(i)=norm(y5(:,i)-T_train(:,i));
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
%绘制逼近误差曲线
plot(1:7,error1,'-*');
hold on;
plot(1:7,error2,'-+');
hold on;
plot(1:7,error3,'-h');
hold on;
plot(1:7,error4,'-d');
hold on;
plot(1:7,error5,'-o');
hold off;
figure;
%绘制预测误差曲线
plot(1:2,error6,'-*');
hold on;
plot(1:2,error7,'-+');
hold on;
plot(1:2,error8,'-h');
hold on;
plot(1:2,error9,'-d');
hold on;
plot(1:2,error10,'-o');
hold off;