"Carlos Aragon" wrote in message <firstname.lastname@example.org>... > Greg, thanks in advance. You're helping a lot! > > You said: > > (..) > > The best is to use a modication of NEWRB that allows the input of an initial > > hidden layer. Then > > > > 1. After training with set1, use those weights as initial weights for training with set2 + set1.
or, if you are lucky
> > 2. After training with set1, use those weights as initial weights for training with set2 and a "characteristic subset" of set1. The drawback is how to define that characteristic. > > > > The reason this works is that each hidden node basis function has local region of influence and a 1-to-1 correspondence with a previous worst classified training vector. > > (...) > > I'm facing problems to perform this action on matlab.
That statement is absolutely useless. I thought you wanted my help.
> Is there any automated way there i can record set1 and then use it to train a set2?
I have no idea what the second part of that statement means.
>How could i do it? Actualy, i want my feedforwardnet to recognize 14 sets of diferent motor loads.
Then simultaneously train on samples or characteristic exemplars from all 14.
If all of the data is not available at once, do it in stages.