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Re: How to display the actual and predicted value of training dataset in NARX
Posted:
Feb 13, 2013 6:30 PM
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Subject: How to display the actual and predicted value of training dataset in NARX
From: Arga Ridhalla
Date: 7 Feb, 2013 15:50:11
Message: 3 of 4
"Greg Heath" <heath@alumni.brown.edu> wrote in message
<kf06f4$bd7$1@newscl01ah.mathworks.com>...
> "Arga Ridhalla" <arga.ridhalla@yahoo.co.id> wrote in message
<ker31p$85u$1@newscl01ah.mathworks.com>...
> > Hi all,
> > I'm a beginner in NN. I have dataset contain 8 time-series input variables and
1 time-series output variable (all of them are representing 60 timesteps). I want
MATLAB to display all the actual value and predicted value that the NN trained it
before. I also want MATLAB to display the future prediction of the output variable f
or 6 timesteps ahead. Please help me how to get that.
> >
> > Thanks for the help!
>
> Post your code so that we can help.
>
> Greg
Hi, Greg! Here's the code:
%
% S=load('nanas Dataset full');
% X=con2seq(S.S.nanasInputReducted);
% T=con2seq(S.S.nanasTargetCopy);
% % Create a Nonlinear Autoregressive Network with External Input
% inputDelays = 12;
% feedbackDelays = 12;
Why did you choose 12?
Did you look at the statistically significant lags of the autocorrelation of T
and crosscorrelation of X and T ?
% hiddenLayerSize = 10;
% net = narxnet(1:inputDelays,1:feedbackDelays,hiddenLayerSize);
% net.trainFcn='traingdm';
% net.trainParam.epochs=10000;
% net.trainParam.lr=1;
% net.trainParam.mc=1
Delete the last 4 commands and accept the narxnet defaults.
% net.trainParam.max_fail=100;
Delete: This is ~ a factor of 20 too high if you are going to use a
validation set for validation stopping. Accept the default of 6.
% net.layers{1}.transferFcn ='logsig';
Delete. Accept the default of 'tansig' which is more appropriate for
hidden layers.
% % Prepare the Data for Training and Simulation
% [inputs,inputStates,layerStates,targets] = preparets(net,X,{},T);
whos X T inputs inputStates layerStates targets
This will confirm if you have the correct dimensions
% % Setup Division of Data for Training, Validation, Testing
% net.divideParam.trainRatio = 70/100;
% net.divideParam.valRatio = 15/100;
% net.divideParam.testRatio = 15/100;
Delete. These are defaults.
However, you are accepting the default DIVIDERAND which
will destroy the correlations you need. Use DIVIDEBLOCK instead.
% % Train the Network
% [net,tr] = train(net,inputs,targets,inputStates,layerStates);
Look at
tr =tr
and choose what you want for outputs.
Hope this helps.
Greg
P.S. I search the newsgroup once or twice a day using "neural".
However, your post was never listed. I was looking for something
I wrote previously and searched using "greg". Only then did your
post appear. Otherwise I would have replied much sooner....
Sorry
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