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用MATLAB仿真的BP神经网络

le0=[1 1 1 1 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 1 1 1]';
le1=[0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0]';
le2=[1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1]';
le3=[1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1]';
le4=[1 0 0 0 1 1 0 0 0 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 1]';
le5=[1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1]';
le6=[1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1]';
le7=[1 1 1 1 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0]';
le8=[1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1]';
le9=[1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1]';
P=[le0,le1,le2,le3,le4,le5,le6,le7,le8,le9];
T=[0 0 0 0;0 0 0 1;0 0 1 0;0 0 1 1;0 1 0 0;0 1 0 1;0 1 1 0;0 1 1 1;1 0 0 0;1 0 0 1]';
%adopting feedforward BP network algorithm %and the BP algorithm training function %Levenberg_Marquardt
net=newff(minmax(P),[10,4],{'logsig','logsig'},'trainlm');
%the biggest training times is 5000 here net.trainParam.epochs=5000;
net.trainParam.goal=0.1;
net.trainParam.lr=0.01;
net.trainParam.mm=0.95;
net.trainParam.er=1.05
net=train(net,P,T);