MATLAB BP神经网络算法

threshold=[0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;];
net=newff(threshold,[146,1],{'tansig','logsig'},'trainscg');
net.trainParam.epochs=10000;
net.trainParam.goal=0.001;
net.trainParam.mc=0.9;
net.trainParam.show=500;
LP.lr=0.1;
net=train(net,P,T);
P_test=[0.578947368 0.931343284 0.184210526 0.541809455 0.795275591 0.620689655 0.74    0.023   0.294988996 0.101234568
0.589473684 0.907462687 0.201754386 0.475382787 0.787401575 0.551724138 0.69    0.025   0.290505071 0.219753086
0.543859649 0.811940299 0.175438596 0.457094396 0.763779528 0.448275862 0.75    0.026   0.54648456  0.192270531
0.368421053 0.731343284 0.245614035 0.495194249 0.488188976 0.413793103 0.61    0.024   0.332154806 0.140740741
0.326315789 0.72238806  0.219298246 0.625509133 0.456692913 0.517241379 0.57    0.019   0.644028101 0.248484848
0.259649123 0.713432836 0.087719298 0.742785605 0.275590551 0.655172414 0.73    0.016   0.365875319 0.140740741
0.231578947 0.776119403 0.192982456 0.653847485 0.480314961 0.551724138 0.86    0.018   0.107613631 0.490058479
0.287719298 0.817910448 0.236842105 0.735574094 0.535433071 0.482758621 0.75    0.016   0.285517334 0.164444444
0.207017544 0.737313433 0.192982456 0.980765459 0.472440945 0.344827586 0.84    0.012   0.634943125 0.377777778
0.185964912 0.746268657 0.184210526 0.735574094 0.448818898 0.310344828 0.62    0.016   0.442964986 0.733333333
]';