Neural network expert system shell

Abstract

This paper presents the architecture of a hybrid neural network expert system shell. The system, structured around the concept of 'network element', is aimed at preserving semantic structure of the expert system rules whilst incorporating 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 the rule-tree during inferencing process. The system is also able to adjust its inference strategy according to different users and situations. A rule editor is also provided to enable easy maintenance of the neural network rule elements.