title: Neural Networks for integer values
body: <p>I have approximately 5000 integer vectors (=SIZE) that look like:<br/></p>
  
  <pre><code>[1 0 4 2 0 1 3 ...]
  </code></pre>
  
  <p>They have the same length N=32 and their values ranges from 0 to 4 but let's say [0 MAX].<br/><br/>
  I created a NN that takes vectors as inputs and outputs a binary array corresponding to one of the desired output(number of possible outputs = M):<br/>
  for instance [0 1 0 0 ...0] => 2nd output. array_length = M<br/>
  I used a Multi Layer Perceptron in Neuroph with those integer values but it did not converge. <br/>
  So I am guessing the problem is using integer values or using a MLP with 3 layers: input, hidden and output.</p>
  
  <p>Can you advise me on the network structure? which type of NN is suitable? Should I remodel the input and output to simplify the learning process? I have been thinking about Gray encoding for the integers input.</p>