Guidelines and Rules of Thumb

In this section we deal with the issue of when to use ANN as compared to more conventional approaches, together with guidelines on how to configure an MLP to obtain optimal results. The recommendations are based on experience and references in the literature. A few of the techniques and ideas discussed here are not implemented in JETNET 3.0 but we have nevertheless chosen to include them in our discussion due to their general interest.

• Choosing the Model and its Parameters
  • ANN versus other methods
  • Choice of ANN Model
  • Number of hidden units
  • Number of hidden layers
  • The activation function
  • Exploiting symmetries
  • Modular methods
• Choosing the Learning Algorithm
  • Back-propagation
  • Quickprop
  • Rprop
  • Conjugate Gradients
• Preprocessing the data
• When to Stop Training
• Regularization and Pruning