Predict Output using Neural Network

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Daniel
Daniel 2011-7-14
回答： khu 2016-9-30
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.
  2 个评论
Mahmood Soltani
Mahmood Soltani 2016-2-19
Daniel, I have the same question, however I am not that good with Matlab, I did not get the answers. Can you give me some help to how give the input and ask for the output out of them? Giving the exact code you entered will be great. Thanks,
Greg Heath
Greg Heath 2016-2-20
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.
Greg

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采纳的回答

altaf adil
altaf adil 2011-7-14
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.

更多回答（2 个）

khu
khu 2016-9-30
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
Ganesh 2011-7-15
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
figure(i+X_tr)
[m(i),b(i),r(i)] = postreg(an_train(i,:),ttr(i,:))
k = ['Regression' int2str(i) '.dat'];
saveas(gcf,k, 'tiffn')
end
Run the program at least 4 to 5 times and take the output with maximum "R" value.

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