"Carlos Aragon" wrote in message <email@example.com>... > "Greg Heath" <firstname.lastname@example.org> wrote in message <email@example.com>... > > PLEASE, PLEASE DO NOT TOP POST!!! > > > > "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. > > I have all the training and test data, but i dont know how could i do to train 14 training vectors and then validate it with just 1 set to check if the neural net is generalizing well.
Not even close. See below.
> Tying to be clear about wat i'm doing. here is the code: > > ia=linear_train_1(1:5001,4); > w=linear_train_1(1:5001,5); > tq=linear_train_1(1:5001,2); > T1=[198:0.000799840032:202]; % Voltage is between 198V and 202V > iateste1=ia_lin_1(1:5001,4); > wteste1=ia_lin_1(1:5001,5);
Seems finely spaced. Do you reallyy need this much data? See below.
> P=[T1;ia';w']; % This is the training vector that in this case, trains just 1 set of data. > T=[tq']; I want my neural net to recognize 14 samples of [T1;ia;w']. T1 is fix but 'ia'' and 'w'' varies according to the load equation i'm changing on my motor model. The question is How could i train it to recognize those 14 samples? If i make 'Ia'' and 'w'' a matrix of 14 different currents and speed, this neural net do not allow me to test a simple >vector like is below
You need to test matrices not single vectors..
> net=feedforwardnet([5 25],'trainbr');
Why 2 hidden layers??? Why H =25 ?? Why 'trainbr?
> net.trainParam.goal = 0.005; %error
> net.trainParam.epochs = 2000;
[ net tr ] = ... MSEtrn = ? MSEval =? MSEtst = ?
Otherwise, how do you obtain separate tr/val/tst results.
> P1=[T1;iateste1';wteste1']; > Y = sim(net,P1); > > As you can see, i'm not an expert on this ... i imagine if you could help me build this process of train and validate. Thanks a lot for your help!
This is post No. 8 of this thread and you don't seem to be any further along than you were at the first post. So, let's start again
1. What is a motor model? 2. What is a motor load? 3. What are V, ia, w and tq ? 4.What are the corresponding correlation coefficients? 5. What , exactly, are the differences between the 14 data sets? 6. Have you plotted the output to determine how much sample spacing is needed to adequately characterize it? 7. Given that spacing, how much data is needed for that characterization? 8. Your first post mentions 10,006 measurements but later you use 5,0001. Is that for each of the 14 data sets? 9. As I stated before 1. Only 1 hidden layer is necessary 2. If you have 14 scenarios that you want to characterize with one net: a. Take 6 and 7 into consideration and combine samples of all 14 into multiple mixed subsets. b. Since you have a large data set, Train/Validate and Test with a 0.34/0.33/0.33 data split. c. Use one or more data sets, as many defaults as possible, and vary H to find the minimum acceptable value.