A neural network expert system shell

Abstract

Presents the architecture of a hybrid neural network expert system shell. The system, structured around the concept of a "network element", is aimed at preserving the semantic structure of the expert system rules whilst incorporating the learning capability of neural networks into the inferencing mechanism. Using this architecture, every rule of the knowledge base is represented by a one- or two-layer neural network element. These network elements are dynamically linked up to form a rule-tree during the inferencing process. The system is also able to adjust its inferencing strategy according to different users and situations. A rule editor is also provided to enable easy maintenance of the neural network rule elements.<>