matlab运行BP神经网络

p=[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0357 0.0214 0.0543 0.0429 0.0357 0.0543 0.0543 0.0357 0.0357 0.06 0.0357 0.0557 0.0914 0.1143 0.1014 0.0714 0.0657 0.0471 0.0471 0.0471 0.0557 0.0714 0.0429 0.1086 0.0857 0.0714 0.1086 0.1086 0.0714 0.0714 0.12 0.0714 0.1114 0.1829 0.2286 0.2029 0.1429 0.1314 0.0943 0.0943 0.0943 0.1114 0.1429 0.0857 0.2171 0.1714 0.1429 0.2171 0.2171 0.1429 0.1429 0.24 0.1429 0.2229 0.3657 0.4571 0.4057 0.2857 0.2629 0.1886 0.1886 0.1886 0.22286 0.1786 0.1071 0.2714 0.2143 0.1786 0.2714 0.2714 0.1786 0.1786 0.3 0.1786 0.2786 0.4571 0.5714 0.5071 0.3571 0.3286 0.2357 0.2357 0.2357 0.2786 0.2143 0.1286 0.3257 0.2571 0.21429 0.3257 0.3257 0.2142 0.2142 0.36 0.2142 0.3342 0.5486 0.6857 0.6086 0.4286 0.3943 0.2829 0.2829 0.2829 0.3343 0.25 0.15 0.38 0.3 0.25 0.38 0.38 0.25 0.25 0.42 0.25 0.39 0.64 0.8 0.71 0.5 0.46 0.33 0.33 0.33 0.39 0.3043 0.21 0.4157 0.3286 0.3043 0.4157 0.4157 0.3043 0.3043 0.5014 0.3043 0.4757 0.69 0.8271 0.75 0.57 0.5357 0.4243 0.3771 0.3771 0.4757 0.3586 0.27 0.4514 0.3571 0.3586 0.4514 0.4514 0.3586 0.3586 0.5829 0.3586 0.5614 0.74 0.8543 0.79 0.64 0.6114 0.5186 0.4243 0.4243 0.5614 0.4671 0.39 0.5229 0.4143 0.4671 0.5229 0.5229 0.4671 0.4671 0.7457 0.4671 0.7329 0.84 0.9086 0.87 0.78 0.7629 0.7071 0.5186 0.5186 0.7329 0.5214 0.45 0.5586 0.4429 0.5214 0.5586 0.5586 0.5214 0.5214 0.8271 0.5214 0.8186 0.89 0.9357 0.91 0.85 0.8386 0.8014 0.5657 0.5657 0.8186 0.5757 0.51 0.5943 0.4714 0.5757 0.5943 0.5943 0.5757 0.5757 0.9086 0.5757 0.9043 0.94 0.9629 0.95 0.92 0.9143 0.8957 0.6129 0.6129 0.9043 0.63 0.57 0.63 0.5 0.63 0.63 0.63 0.63 0.63 0.99 0.63 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.66 0.66 0.99 0.7357 0.6929 0.7357 0.6429 0.7357 0.7357 0.7357 0.7357 0.7357 0.9929 0.7357 0.9929 0.9929 0.9929 0.9929 0.9929 0.9929 0.9929 0.7571 0.7571 0.9929 0.7886 0.7543 0.7886 0.7143 0.7886 0.7886 0.7886 0.7886 0.7886 0.9943 0.7886 0.9943 0.9943 0.9943 0.9943 0.9943 0.9943 0.9943 0.8057 0.8057 0.9943 0.8414 0.8157 0.8414 0.7857 0.8414 0.8414 0.8414 0.8414 0.8414 0.9957 0.8414 0.9957 0.9957 0.9957 0.9957 0.9957 0.9957 0.9957 0.8543 0.8543 0.9957 0.9473 0.9386 0.9471 0.9286 0.9471 0.9471 0.9471 0.9471 0.9471 0.9986 0.9471 0.9986 0.9986 0.9986 0.9986 0.9986 0.9986 0.9986 0.9514 0.9514 0.9986 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ]'; t=[0.1 0.11429 0.12857 0.15714 0.17143 0.18571 0.2 0.21429 0.22857 0.25714 0.27143 0.28571 0.3 0.32857 0.34286 0.35714 0.38571 0.4]; threshold=[0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1]; net=newff(threshold,[10,1],{'tansig','logsig'},'trainlm') net.trainParam.epochs=1000; net.trainParam.goal=0.00001; LP.lr=0.00001; net=train(net,p,t); p_test=[0.1071 0.0643 0.1629 0.1286 0.1071 0.1629 0.1629 0.1071 0.1071 0.18 0.1071 0.1671 0.2743 0.3429 0.3043 0.2143 0.1975 0.1415 0.1414 0.1414 0.1671 0.4129 0.33 0.4871 0.3857 0.4129 0.4871 0.4871 0.4129 0.4129 0.6643 0.4129 0.6471 0.79 0.8814 0.83 0.71 0.6871 0.6129 0.4714 0.4714 0.6471 0.6829 0.6314 0.6829 0.5714 0.6829 0.6829 0.6829 0.6829 0.6829 0.9914 0.6829 0.9914 0.9914 0.9914 0.9914 0.9914 0.9914 0.9914 0.7086 0.7086 0.9914 0.8943 0.8771 0.8943 0.8571 0.8943 0.8943 0.8943 0.8943 0.8943 0.9971 0.8943 0.9971 0.9971 0.9971 0.9971 0.9971 0.9971 0.9971 0.9029 0.9029 0.9971 ]'; y=sim(net,p_test)