clear all;
p=[-1:0.05:1];
t0=sin(2*pi*p);
t=sin(2*pi*p)+0.1*randn(size(p));
>> val.P=[-0.975:0.05:0.975];
>> val.T=sin(2*pi*val.P)+0.1*randn(size(val.P));
>> net=newff([-1 1],[20 1],{'tansig','purelin'},'traingdx');
>> net.trainParam.show=25;
>> net.trainParam.epochs=300;
>> net=init(net);
>> [net,tr]=train(net,p,t,[],[],val);
TRAINGDX, Epoch 0/300, MSE 2.61268/0, Gradient 4.47951/1e-006
TRAINGDX, Epoch 25/300, MSE 0.781523/0, Gradient 1.39561/1e-006
TRAINGDX, Epoch 50/300, MSE 0.140987/0, Gradient 0.327862/1e-006
TRAINGDX, Epoch 75/300, MSE 0.0394844/0, Gradient 0.0872358/1e-006
TRAINGDX, Epoch 100/300, MSE 0.0127487/0, Gradient 0.0257514/1e-006
TRAINGDX, Epoch 125/300, MSE 0.00729413/0, Gradient 0.117012/1e-006
TRAINGDX, Epoch 139/300, MSE 0.00685727/0, Gradient 0.0311852/1e-006
TRAINGDX, Validation stop.

>> save ES net;
>> clear all;
>> p=[-1:0.05:1];
t0=sin(2*pi*p);
t=sin(2*pi*p)+0.1*randn(size(p));
>> load ES net;
>> r=sim(net,p);
>> hold on
>> plot(p,t0,':r');
>> plot(p,t,'*b');
>> plot(p,r,'-k');
>> hold off