What version of the NN toolbox are you using? The latest versions have
fitnet for regression or curvefitting
patternnet for classification and pattern recognition
The previous versions were newfit and newpr, respectively.
All versions have a default trn/val/tst (0.7/0.15/0.15) random division option for validation stopping(val) and unbiased prediction(tst). Various other combinations of data division types and percent ratios are available via overwriting the defaults.
lookfor divide
Another way to avoid overtraining an overfit net is to make sure the number of output training equations Neq = prod(size(targets)) = O*N is significantly larger than the number of unknown weights Nw = net.numWeightElements = (I+1)*H+(H+1)*O for an I-H-O FFMLP.
Neq >> Nw ==> H << (Neq-O)/(I+O+1)
Hope this helps.
Thank you for officially accepting my answer(;>)
