Towards integrating rule-based expert systems and neural networks

Abstract

This research explores a new approach to integrate neural networks and expert systems. The integrated system combines the strength of rule-based semantic structure and the learning capability of connectionist architecture. In addition, the approach allows users to define logical operators that behave much similar to that of human expert decision making process. Neural Logic Network (NEULONET) is used as the underlying building unit. A rule-based shell like environment is developed. The shell is used to built a prototype expert decision support system for future bonds trading. The system also provides a way to behave like different experts responding to different users and giving advice according to different environmental situations.