TWO ANALYSIS TECHNIQUES FOR FEED-FORWARD NETWORKS

Abstract

Two methods of obtaining structured information from a trained feed-forward neural network are discussed. The first of these methods extracts structured representations from the hidden layer and tests these against a set of hypotheses. The second method uses a rule-based system based on multiple-valued logic that can be trained using gradient descent.