neural network on matlab performance problem

I'm using this code to do a NN in order to train my network to give me the classifications on images:

net = newff(p,t,15,{},'traingd');
net.divideParam.trainRatio = 70/100;  % Adjust as desired
net.divideParam.valRatio = 15/100;  % Adjust as desired
net.divideParam.testRatio = 15/100;  % Adjust as desired
net.trainParam.epochs = 10000;
net.trainParam.goal = 0.01;
net.trainParam.show = 25;
net.trainParam.time = inf;
net.trainParam.min_grad = 1e-10;
net.trainParam.max_fail = 10;
net.trainParam.sigma = 5.0e-5;
net.trainParam.lambda = 5.0e-7;
net.trainParam.mu_max = 1e-20;
net.trainParam.lr = 0.001; 

% Train and Apply Network
[net,tr] = train(net,p,t);
outputs = sim(net,p);

% Create P.

% Plot
plotperf(tr)
plotfit(net,p,t)
plotregression(t,outpts)

But my performance never goes bellow 0.5. Tryed to do PCA on the data but I think somethin开发者_Go百科g is not right on the code? Is it possible to change the initial value of the performance that shows on the nntraintool?

thank you Paulo

It's hard to say without having your data, but from my experience with neural nets only one of a few things can possibly be happening:

1. You don't have enough hidden nodes to represent your data
2. Your time step is too high
3. Your error space is complicated due to your data and you're reaching lots of local minima. This is a similar but slightly different way of saying 1.
4. Your data is degenerate, in that you have training samples with different labels but exactly the same features.

If 1, then increase the number of hidden nodes. If 2, decrease the time step If 3, you can try initializing better with Nguyen-Widrow initialization perhaps (this used to be in the function initnw.) If 4, figure out why your data is like this and fix it.

Thanks to @sazary for pointing out some details about initnw being the default when you create a new network with newff or newcf.