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Topic: Training multiple data for a single feedforwardnet
Replies: 19   Last Post: Dec 1, 2012 6:14 PM

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 Carlos Aragon Posts: 11 Registered: 10/17/12
Re: Training multiple data for a single feedforwardnet
Posted: Nov 1, 2012 6:28 PM

"Greg Heath" <heath@alumni.brown.edu> wrote in message <k6rgsg\$sp3\$1@newscl01ah.mathworks.com>...
>
> "Carlos Aragon" wrote in message <k6mhuh\$etk\$1@newscl01ah.mathworks.com>...

> > 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.

> What do you not understand about that?
>
> Hope that is clear.
>
> Greg

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);
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.

net=feedforwardnet([5 25],'trainbr');
net.trainParam.goal = 0.005; %error
net.trainParam.epochs = 2000;
net=train(net,P,T);
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!

Carlos.

Date Subject Author
10/17/12 Carlos Aragon
10/19/12 Greg Heath
10/20/12 Greg Heath
10/29/12 Carlos Aragon
10/31/12 Greg Heath
11/1/12 Carlos Aragon
11/1/12 Greg Heath
11/3/12 Carlos Aragon
11/3/12 Greg Heath
11/5/12 Carlos Aragon
11/16/12 Carlos Aragon
11/17/12 Greg Heath
11/17/12 Carlos Aragon
11/17/12 Greg Heath
11/17/12 Carlos Aragon
11/17/12 Greg Heath
11/18/12 Carlos Aragon
11/18/12 Greg Heath
12/1/12 Carlos Aragon
10/29/12 Carlos Aragon