c8_5bfshebeizhuangtaifenleiqi.m

根据神经网络的相关原理

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 -0.11938 -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;
    -1.0242 -0.1461 -0.1018 -0.0778 -0.0363 -0.0476 -0.0160 -0.0253;
     0.1605 -0.092 -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;
     -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]';
 net=newff(minmax(P),[17 2],{'tansig','logsig'},'trainlm');
 net.trainParam.epochs=50;
  net.trainParam.goal=0.001;
 net=train(net,P,T);
 sim(net,P); %检验网络输出是否满足要求，运行结果：
 %0.0000    0.0314    0.0225    0.0273    0.9948    0.9972    0.9968    0.9946    0.9996    1.0000    1.0000    0.9997
 %0.9998    0.9744    0.9794    0.9786    0.0684    0.0933    0.0382    0.0552    0.9931    1.0000    1.0000    0.9999
 %对照T，可见误差很小满足设计要求
 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]';%分别在设备正常、
                %轻微故障、严重故障时的3组状态数据
 Y=sim(net,P_test)%结果  0.0000    0.9899    0.9995
                       % 0.9863    0.0860    0.9906对应（0，1）（1,0）（1,1）