Shell environment for developing connectionist decision support systems

Abstract

This paper presents the architecture of a neural network expert system shell. The system captures every rule as a rudimentary neural network, which is called a network element (netel). The aim is to preserve the semantic structure of the expert system rules, while incorporating the learning capability of neural networks into the inferencing mechanism. These netel rules are dynamically linked up to form the rule-tree during the inferencing process, just as a conventional expert system does. The system is also able to adjust its inference strategy according to different users and situations. A rule editor is provided to enable easy maintenance of the netel rules. These components are housed under a user-friendly interface. An application expert system for US future bonds trading is built upon this shell. The connectionist expert system has demonstrated its strength over the conventional rule-based system.