bp_class.m

采用BP网络进行分类

% P和T列数必须相等，P的行数代表输入层有多少个神经元
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  -0.1489;
   -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';
% 新建一个BP网络
net = newff(P,T,[17,2],{'tansig','logsig'},'trainlm');
net.trainParam.epochs = 50;
net.trainParam.goal = 0.001;
% 用P，T来训练网络
net = train(net,P,T);
% 用几个样本来测试网络
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)