Optimization with Artificial Neural Network Systems: A Mapping Principle and a Comparison to Gradient Based Methods

A comparison is made to optim(cid:173) ization using gradient-search methods. The perfonnance measure is the settling time from an initial state to a target state. A simple analytical example illustrates a situation where dynamical systems representing artificial neural network methods would settle faster than those representing gradient(cid:173) search. Settling time was investigated for a more complicated optimization problem using com(cid:173) puter simulations. The problem was a simplified version of a problem in medical imaging: deter(cid:173) mining loci of cerebral activity from electromagnetic measurements at the scalp.