Predict Output using Neural Network

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Daniel on 14 Jul 2011
Answered: khu on 30 Sep 2016
Hello everyone, I have a data set which has 60 rows and 105 columns, 100 of these columns are input and 5 are outputs for the 60 elements of my data set. I would like to have a neural network which, when given the 100 input parameters, can generate the 5 output parameters based on the information that I have on the 60 elements of my current data set.
Using the neural networks pattern recognition toolbox I am able to create the neural network, but I do not know how to use it to predict other values based on just input.
In other words, how can I use neural networks to predict output based on input.
Thank you.
Greg Heath
Greg Heath on 20 Feb 2016
Did you notice that Daniel's post is 5 years old?
For classification examples use the commands
help patternnet
doc patternnet
Then search BOTH the NEWSGROUP and ANSWERS for patternnet posts
patternnet tutorial
patternnet greg
More examples of classification data can be obtained using
help nndatasets
doc nndatasets
Hope this helps.

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Accepted Answer

altaf adil
altaf adil on 14 Jul 2011
You can use the function "sim" that simulates the neural network.
Just pass the trained network and test samples to the function.
Test samples can be different observations other that you have used to train the network.

More Answers (2)

khu on 30 Sep 2016
Nerual network toolbox creates a network based on your training dataset.
Assuming the network is named as "net" and input set for which you need output is "x", you can get the output with the following command: >> net(x)

Ganesh on 15 Jul 2011
You can use 75% data for testing and 25% for training the network. Select proper algorithm for training the network according to your application. You can use following code to observe "R" value with plots.
an_train = sim(net,ptr);
a_train = poststd(an_train,meant,stdt);
[X_tr,Y_tr] = size(an_train);
for i = 1:X_tr
[m(i),b(i),r(i)] = postreg(an_train(i,:),ttr(i,:))
k = ['Regression' int2str(i) '.dat'];
saveas(gcf,k, 'tiffn')
Run the program at least 4 to 5 times and take the output with maximum "R" value.

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