ann40.m

神经网络ANN基于matlab的分类程序

P=[5.1	4.9	4.7	4.6	5	5.4	4.6	5	4.4	4.9	5.4	4.8	4.8	4.3	5.8	5.7	5.4	5.1	5.7	5.1	5.4	5.1	4.6	5.1	4.8	5	5	5.2	5.2	4.7	4.8	5.4	5.2	5.5	4.9	5	5.5	4.9	4.4	5.1	7	6.4	6.9	5.5	6.5	5.7	6.3	4.9	6.6	5.2	5	5.9	6	6.1	5.6	6.7	5.6	5.8	6.2	5.6	5.9	6.1	6.3	6.1	6.4	6.6	6.8	6.7	6	5.7	5.5	5.5	5.8	6	5.4	6	6.7	6.3	5.6	5.5	6.3	5.8	7.1	6.3	6.5	7.6	4.9	7.3	6.7	7.2	6.5	6.4	6.8	5.7	5.8	6.4	6.5	7.7	7.7	6	6.9	5.6	7.7	6.3	6.7	7.2	6.2	6.1	6.4	7.2	7.4	7.9	6.4	6.3	6.1	7.7	6.3	6.4	6	6.9;
3.5	3	3.2	3.1	3.6	3.9	3.4	3.4	2.9	3.1	3.7	3.4	3	3	4	4.4	3.9	3.5	3.8	3.8	3.4	3.7	3.6	3.3	3.4	3	3.4	3.5	3.4	3.2	3.1	3.4	4.1	4.2	3.1	3.2	3.5	3.1	3	3.4	3.2	3.2	3.1	2.3	2.8	2.8	3.3	2.4	2.9	2.7	2	3	2.2	2.9	2.9	3.1	3	2.7	2.2	2.5	3.2	2.8	2.5	2.8	2.9	3	2.8	3	2.9	2.6	2.4	2.4	2.7	2.7	3	3.4	3.1	2.3	3	2.5	3.3	2.7	3	2.9	3	3	2.5	2.9	2.5	3.6	3.2	2.7	3	2.5	2.8	3.2	3	3.8	2.6	2.2	3.2	2.8	2.8	2.7	3.3	3.2	2.8	3	2.8	3	2.8	3.8	2.8	2.8	2.6	3	3.4	3.1	3	3.1;
1.4	1.4	1.3	1.5	1.4	1.7	1.4	1.5	1.4	1.5	1.5	1.6	1.4	1.1	1.2	1.5	1.3	1.4	1.7	1.5	1.7	1.5	1	1.7	1.9	1.6	1.6	1.5	1.4	1.6	1.6	1.5	1.5	1.4	1.5	1.2	1.3	1.5	1.3	1.5	4.7	4.5	4.9	4	4.6	4.5	4.7	3.3	4.6	3.9	3.5	4.2	4	4.7	3.6	4.4	4.5	4.1	4.5	3.9	4.8	4	4.9	4.7	4.3	4.4	4.8	5	4.5	3.5	3.8	3.7	3.9	5.1	4.5	4.5	4.7	4.4	4.1	4	6	5.1	5.9	5.6	5.8	6.6	4.5	6.3	5.8	6.1	5.1	5.3	5.5	5	5.1	5.3	5.5	6.7	6.9	5	5.7	4.9	6.7	4.9	5.7	6	4.8	4.9	5.6	5.8	6.1	6.4	5.6	5.1	5.6	6.1	5.6	5.5	4.8	5.4;
0.2	0.2	0.2	0.2	0.2	0.4	0.3	0.2	0.2	0.1	0.2	0.2	0.1	0.1	0.2	0.4	0.4	0.3	0.3	0.3	0.2	0.4	0.2	0.5	0.2	0.2	0.4	0.2	0.2	0.2	0.2	0.4	0.1	0.2	0.1	0.2	0.2	0.1	0.2	0.2	1.4	1.5	1.5	1.3	1.5	1.3	1.6	1	1.3	1.4	1	1.5	1	1.4	1.3	1.4	1.5	1	1.5	1.1	1.8	1.3	1.5	1.2	1.3	1.4	1.4	1.7	1.5	1	1.1	1	1.2	1.6	1.5	1.6	1.5	1.3	1.3	1.3	2.5	1.9	2.1	1.8	2.2	2.1	1.7	1.8	1.8	2.5	2	1.9	2.1	2	2.4	2.3	1.8	2.2	2.3	1.5	2.3	2	2	1.8	2.1	1.8	1.8	1.8	2.1	1.6	1.9	2	2.2	1.5	1.4	2.3	2.4	1.8	1.8	2.1
];
T=[0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	;
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	;
1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	
];
net=newff(minmax(P),[6,3],{'logsig','logsig'},'traingdx');
net.trainParam.show=10;
net.trainParam.epochs=1000;
net.trainParam.goal=0.001;
net=train(net,P,T)
A=[5	4.5	4.4	5	5.1	4.8	5.1	4.6	5.3	5	5.5	6.1	5.8	5	5.6	5.7	5.7	6.2	5.1	5.7	6.7	6.9	5.8	6.8	6.7	6.7	6.3	6.5	6.2	5.9;
3.5	2.3	3.2	3.5	3.8	3	3.8	3.2	3.7	3.3	2.6	3	2.6	2.3	2.7	3	2.9	2.9	2.5	2.8	3.1	3.1	2.7	3.2	3.3	3	2.5	3	3.4	3;
1.3	1.3	1.3	1.6	1.9	1.4	1.6	1.4	1.5	1.4	4.4	4.6	4	3.3	4.2	4.2	4.2	4.3	3	4.1	5.6	5.1	5.1	5.9	5.7	5.2	5	5.2	5.4	5.1;
0.3	0.3	0.2	0.6	0.4	0.3	0.2	0.2	0.2	0.2	1.2	1.4	1.2	1	1.3	1.2	1.3	1.3	1.1	1.3	2.4	2.3	1.9	2.3	2.5	2.3	1.9	2	2.3	1.8
];
y=sim(net,A)
B=[0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1;
0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0;
1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
];