title: Neural network doesn't converge - using Multilayer Perceptron
body: <p>I've developed a "Pong" style game which effectively has a ball at the bottom of the screen and bouncy walls on the left and right and a sticky wall on the top. It randomly chooses a point on the bottom (on a straight horizontal line) and a random angle, bounces off the side walls, and hits the top wall. This is repeated a 1000 times and each time, the x-value of the launch position, the launch angle and the final x-value of the position it collides with on the top wall.</p>
  
  <p>This gives me 2 inputs - x-value of launch and launch angle and 1 output - x-value of final position. I tried using a multilayer perceptron with 2 input nodes, 2 hidden nodes (1 layer) and 1 output node. However it converges upto a point ~20 and then tapers off. Here's what I've tried and none of them helped, either the error never converges or it starts diverging:</p>
  
  <ol>
  <li>Transform inputs and output to be between 0 and 1</li>
  <li>Transform inputs and output to be between -1 and 1</li>
  <li>Increase number of hidden layers</li>
  <li>Increase number of nodes in hidden layer</li>
  <li>Convert the launch position, launch angle and final position into 0s and 1s resulting in ~750+175 inputs and ~750 outputs - no convergence</li>
  </ol>
  
  <p>So, after spending all night and morning and making my brain and body revolt against me, I'm hoping someone can help me identify the problem here. Is this a task that's just not solvable by a neural network or am I doing something wrong?</p>
  
  <p>PS: I'm using the online version of <a href="http://neuroph.sourceforge.net/online_demo.html" rel="nofollow">Neuroph</a> and not coding my own procedure. At least this will help me avoid issues in implementation</p>