8-5-1.m

《神经网络理论与Matlab R2007实现》（程序代码）

P=[-1.7817 -0.2786 -0.2954 -0.2394 -0.1842 -0.1572 -0.1584 -0.1998;
    -1.8710 -0.2957 -0.3494 -0.2904 -0.1460 -0.1387 -0.1492 -0.2228;
-1.8347 -0.2817 -0.3566 -0.3476 -0.1820 -0.1435 -0.1778 -0.1849;
-1.8807 -0.2467 -0.2316 -0.2419 -0.1938 -0.2103 -0.2010 -0.2533;
-1.4151 -0.2282 -0.2124 -0.2147 -0.1271 -0.0680 -0.0872 -0.1684;
-1.2879 -0.2252 -0.2012 -0.1298 -0.0245 -0.0390 -0.0762 -0.1672;
-1.5239 -0.1979 -0.1094 -0.1402 -0.0994 -0.1394 -0.1673 -0.2810;
-1.6781 -0.2047 -0.1180 -0.1532 -0.1732 -0.1716 -0.1851 -0.2006;
0.1605 -0.0920 -0.0160  0.1246  0.1802  0.2087  0.2234  0.1003;
0.2045  0.1078 0.2246  0.2031  0.2428  0.2050  0.0704  0.0403;
-1.0242 -0.1461 -0.1018 -0.0778 -0.0363 -0.0476 -0.0160 -0.0253;
-0.7915 -0.1018 -0.0737 -0.0945 -0.0955  0.0044  0.0467  0.0719];
T=[0 1;0 1;0 1;0 1;1 0;1 0;1 0;1 0;1 1;1 1;1 1;1 1];
P=P';
T=T';
net=newff(minmax(P),[17,2],{'tansig','logsig'},'trainlm');
net.trainParam.epochs=50;
net.trainParam.goal=0.001;
net=train(net,P,T);

P_test=[-1.4736 -0.2845 -3.0724 -0.2108 -0.1904 -0.1467 -0.1696 -0.2001;
–1.6002 –0.2011 –0.1021 –0.1394 –0.1001 –0.1572 –0.1584 –0.2790;
-1.0314 -0.1521 -0.1101 -0.0801 -0.0347 -0.0482 -0.0158 -0.0301]';
Y=sim(net,P_test)