The effect of the learning rate.

P = [0.5152 0.8173 1.0000;
     0.8173 1.0000 0.7308;
     1.0000 0.7308 0.1390;
     0.7308 0.1390 0.1087;
     0.1390 0.1087 0.3520;
     0.1087 0.3520 0.0000;]';
T=[0.7308 0.1390 0.1087 0.3520 0.0000 0.3761];
net=newff([0 1;0 1;0 1],[5,1],{'tansig','logsig'},'traingd');
net.trainParam.epochs=1000;
net.trainParam.goal=0.01;
LP.lr=0.01;
net=train(net,P,T);

If the learning rate is too large, it may swing between inappropriate solution.
If the learning rate is too small, the learning will be very slow.

learning rate=0.01:the performance=0.128
learning rate=0.1:the performance=0.185
learning rate=0.2:the performance=0.337
learning rate=0.5:the performance=0.308