Knowledge Extraction from Self-Organizing Neural Networks

Abstract

In this work we present the integration of neural networks with a rule based expert system. The system realizes the automatic acquisition of knowledge out of a set of examples. It enhances the reasoning capabilities of classical expert systems with the ability of generalise and the handling of incomplete cases. It uses neural nets with unsupervised learning algorithms to extract regularities out of case data. A symbolic rule generator transforms these regularities into PROLOG rules. The generated rules and the trained neural nets are embedded into the expert system as knowledge bases. In the system«s diagnosis phase it is possible to use these knowledge bases together with human experts« knowledge bases in order to diagnose a unknown case. Furthermore the system is able to diagnose and to complete inconsistent data using the trained neural nets exploiting their ability to generalise.