PRAM LAYOUT OPTIMIZATION

We propose a procedure for designing the layout of a fixed fan-in neur al network based on the pRAM model. Using minimisation of the relative entropy between environment and output probability distribution laws as a target, and Amari's learning rule as a strategy, we show that con sideration of the conditional entropy of the output of one node, given the candidate nodes that provide its inputs, leads to a locally optim um choice of the the connections. The procedure is computationally fea sible when certain preference criteria are used to control the mutual relevance of the pRAM nodes. We give some simple numerical examples of this procedure. (C) 1997 Elsevier Science Ltd. All rights reserved.