p =[ -2.9593 0.1813
-3.1141 -0.1244
-1.0777 1.1438
-1.1467 1.3820
1.2126 2.1338
1.1497 1.9414
3.1104 -2.6393
3.2008 -3.0024 ]; % 8个训练样本，分成4类
z=zeros(8,1); %z用来存放分类后的结果。
net12=newp(minmax(p'),1);
net12.trainParam.epochs = 500;
net13=newp(minmax(p'),1);
net13.trainParam.epochs = 500;
net14=newp(minmax(p'),1);
net14.trainParam.epochs = 500;
net23=newp(minmax(p'),1);
net23.trainParam.epochs = 500;
net24=newp(minmax(p'),1);
net24.trainParam.epochs = 500;
net34=newp(minmax(p'),1);
net34.trainParam.epochs = 500;
plot(p(:,1)',p(:,2)','*')
p12=[-2.9593 -3.1141 -1.0777 -1.1467
0.1813 -0.1244 1.1438 1.3820 ];
t12=[1 1 0 0];
p13=[-2.9593 -3.1141 1.2126 1.1497
0.1813 -0.1244 2.1338 1.9414 ];
t13=[1 1 0 0];
p14=[-2.9593 -3.1141 3.1104 3.2008
0.1813 -0.1244 -2.6393 -3.0024 ];
t14=[1 1 0 0];
p23=[ -1.0777 -1.1467 1.2126 1.1497
1.1438 1.3820 2.1338 1.9414 ];
t23=[1 1 0 0];
p24= [-1.0777 -1.1467 3.1104 3.2008
1.1438 1.3820 -2.6393 -3.0024 ];
t24=[1 1 0 0];
p34=[1.2126 1.1497 3.1104 3.2008
2.1338 1.9414 -2.6393 -3.0024];
t34=[1 1 0 0];
[net12, tr12]=train(net12,p12,t12);
[net13, tr13]=train(net13,p13,t13);
[net14, tr14]=train(net14,p14,t14);
[net23, tr23]=train(net23,p23,t23);
[net24, tr24]=train(net24,p24,t24);
[net34, tr34]=train(net34,p34,t34);
for m=1:8 % 将训练样本输入进线性分类器，测试结果是否正确
y12=sim(net12,p(m,:)');
y13=sim(net13,p(m,:)');
y14=sim(net14,p(m,:)');
y23=sim(net23,p(m,:)');
y24=sim(net24,p(m,:)');
y34=sim(net34,p(m,:)');
if (y12==1)&(y13==1)&(y14==1)
z(m)=1;
elseif (y12==0)&(y23==1)&(y24==1)
z(m)=2;
elseif(y13==0)&(y23==0)&(y34==1)
z(m)=3;
elseif(y14==0)&(y24==0)&(y34==0)
z(m)=4;
else
end
end
