title: How to properly model ANN to find relationship between real value input-output data?
body: <p>I'm trying to make an ANN which could tell me if there is causality between my input and output data. Data is following: <br/>
  My input are measured values of pesticides (19 total) in an area eg:</p>
  
  <pre> -1.031413662  -0.156086316    -1.079232918    -0.659174849    -0.734577317    -0.944137546    -0.596917991    -0.282641072    -0.023508282    3.405638835 -1.008434997    -0.102330305    -0.65961995 -0.687140701    -0.167400684    -0.4387984  -0.855708613    -0.775964435    1.283238514</pre>
  
  <p>And the output is the measured value of plant-somthing in the same area (55 total) eg:</p>
  
  <pre>0.00   0.00    0.00    13.56   0   13.56   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   13.56   0   0   0   1.69    0   0   0   0   0   0   0   0   0   0   1.69    0   0   0   0   13.56   0   0   0   0   13.56   0   0   0   0   0   0
  </pre>
  
  <p>Values for input are in range from -2.5 to 10, and for output from 0 to 100.
  So the question I'm trying to answer is: in what measure does pesticide A affect plant-somthings. </p>
  
  <p>What are good ways to model (represent) input/output neurons to be able to process the mentioned input/output data? And how to scale/convert input/output data to be useful for NN? </p>
  
  <p>Is there a book/paper that I should look at?</p>