"Carlos Aragon" wrote in message <email@example.com>... > I'm building a feedforwardnet like this: > > (..) > P=[V';ia';w']; > T=[tq']; > net=feedforwardnet([5 25],'trainbr');
One hidden layer with H nodes is sufficient. Try to minimize H by trial and error. Start with 10 small values of H and Ntrials = 10 of random initial weights for each value of H. For examples search using some of the following keywords:
heath close clear Ntrials Neq Nw
> (..) > > How could i train this neural net for more then one group '[V';ia';w']' ? How is the matlab structure to perform this kind of training?
For I dimensional inputs and O-dimensional outputs
[ I N ] = size(input) [ O N] = size(target)
Neq = N*O training equations for estimating
Nw = (I+1)*H+(H+1)*O weights.
Neq >= Nw when
H <= (Neq-O)/(I+O+1)
> Note that 'P' in this case is a 10006x3 matrix that i extract from a motor model.
That is a large number of samples. You can probably use a simple 0.34/0.33/0.33 trn/val/tst random data split, net.divideFcn = 'dividetrain' and 'trainlm'.