On Predicting the Optimal Number of Hidden Nodes
Alan J Thomas, Miltos Petridis, Simon D. Walters, Saeed Malekshahi Gheytassi, Robert E. Morgan · 2015
Determining the optimal number of hidden nodes is the most challenging aspect of Artificial Neural Network (ANN) design. To date, there are still no reliable methods of determining this a priori, as it depends on so many domain-specific factors. Current methods which take these into account, such as exhaustive search, growing and pruning and evolutionary algorithms are not only inexact, but also extremely time consuming -- in some cases prohibitively so. A novel approach embodied in a system called Heurix is introduced. This rapidly predicts the optimal number of hidden nodes from a small number of sample topologies. It can be configured to favour speed (low complexity), accuracy, or a balance between the two. Single hidden layer feedforward networks (SLFNs) can be built twenty times faster, and with a generalisation error of as little as 0.4% greater than those found by exhaustive search.