A SHELL ENVIRONMENT FOR DEVELOPING CONNECTIONIST DECISION-SUPPORT SYSTEMS

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 th e semantic structure of the expert system rules, while incorporating t he learning capability of neural networks into the inferencing mechani sm. These netal rules are dynamically linked up to form the rule-tree during the inferencing process, just as a conventional expert system d oes. The system is also able to adjust its inference strategy accordin g to different users and situations. A rule editor is provided to enab le easy maintenance of the netal rules. These components are housed un der a user-friendly interface. An application expert system for US fut ure bonds trading is built upon this shell. The connectionist expert s ystem has demonstrated its strength over the conventional rule-based s ystem.