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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 3, 2012 5:33 PM

"Greg Heath" <heath@alumni.brown.edu> wrote in message <k6vg6s\$36r\$1@newscl01ah.mathworks.com>...
> "Carlos Aragon" wrote in message <k6ut1o\$1mo\$1@newscl01ah.mathworks.com>...
> > "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.
>
> 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?
>trainbr: i have to use bayesien-regulation

> > net.trainParam.goal = 0.005; %error
>
> Why?

defined goal.
> > net.trainParam.epochs = 2000;
>
> Why?

maximum training epochs that i want, if not reached the trainparam.goal.
> > net=train(net,P,T);
>
> Performance evaluation??
>
> [ net tr ] = ...
> MSEtrn = ?
> MSEval =?
> MSEtst = ?

I dont understand what it means.

> 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

It's a dificult task to explain. The goal of this thread is (code by code) determine how to train different sets of [V;Ia;w] defined above, so that my neural net will recognize those 14 datas.

> 1. What is a motor model?

There's an induction motor machine model. Resuming, i'm extracting data from this model associated with other procediment that does not matter here..

> 2. What is a motor load?

A motor is a device that converts electrical energy into mechanical energy to act upon a mechanical load. The burden placed on the motor due to this mechanical activity is referred to as the motor load.

> 3. What are V, ia, w and tq ?
V -> Voltage
ia -> Current on phase 'a'
w-> motor speed
tq-> it's the load generated according to the type of burden used to train.

> 4.What are the corresponding correlation coefficients?
I think that does'nt matter too

> 5. What , exactly, are the differences between the 14 data sets?
Defined what load is, the difference between the 14 data sets is the type with burden i'm using on the motor.

> 6. Have you plotted the output to determine how much sample spacing is
> needed to adequately characterize it?

5000 datas is enough to have values from the transitory state to steady state.

> 7. Given that spacing, how much data is needed for that characterization?
6.
> 8. Your first post mentions 10,006 measurements but later you use 5,0001.
Yes. I've cut unnecessary data.

> Is that for each of the 14 data sets?
one data set is a value of V ; Ia; W. Only 'V' is fix. Ia nd W varies in each of the 14 data sets. There are 5001 values of Ia and 5001 Values of W as there are 5001 values of fixed V (voltage)

> 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.
>
> This should give you a solid start.
>
> Hope this helps.
>
> Greg

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