I have an input data for neural network that consists of 2 vectors with 200 elements, that i got from some program for generating signals. So it is actually 2x200 input to my nnet. As target data, i have one 1x200 vector that i also got from the same program. That is my training data set. I gather as much of those sets as i want so i transfer them to matlab and save them as, for example, set1, set2,....
When i am creating neural net, using newfit function (backropagation algorithm and everyhting else is set by default because i am kind of unexperianced with neural nets so i will have to experiment) i'm creating it using set1 only for example. Then, when i am to train neural net i train it for set1 then load set2 and train for it and so on.
so its like this
function net = create_fit_net(inputs,targets)
numHiddenNeurons = 20;
net = newfit(inputs,targets,numHiddenNeurons);
net=train(net,inputs,targets);
load set2;
net=train(net,inputs,targets);
load set3;
net=train(net,inputs,targets);
load set4;
net=train(net,inputs,targets);
i am using 4 sets of data here and all sets have variables of same name and size.
My quesion is, am i doing this the right way, because, when doing simulation in some other m file, i am getting bad results, and every time i get different results. Does it matter if i create network with one set and then train with others too, and does it matter what set do i use to train network 1st? Also, i am confused about the amount of neurons to use (im using in the example 20 but actually i tried 1, 10, 30, 50, 100 200 and even 300 and i get nothing). If you have any suggestions, i'd be glad to take them into consideration. Any help is welcome.
thanks in advance
