Framework for integrating fault diagnosis and incremental knowledge acquisition in connectionist expert systems

Abstract

In this paper, we propose a framework for integrating fault diagnosis and incremental knowledge acquisition in connectionist expert systems. A new case solved by the Diagnostic Function is formulated as a new example for the Learning Function to learn incrementally. The Diagnostic Function is composed of a neural networks-based Example Module and a symbolic-based Rule Module. While the Example Module is always first invoked to provide the shortcut solution the Rule Module provides extensive coverage of eases to handle odd cases when Example Module fails. Two applications based on the proposed framework will also be briefly mentioned.