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Proceedings Paper

C-APACS: a connectionist expert system architecture
Author(s): Keith C. C. Chan; John Y. Ching; Andrew K. C. Wong
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Paper Abstract

In this paper, we present an expert system architecture based on a new artificial neural network. Unlike other connectionist approaches, the proposed network paradigm is able to synthesize explicit production rules from the processing elements and the weighted connections of a trained network. The generated rules can be incorporated into an expert system to perform classification tasks such as engineering troubleshooting. The design of the neural network is based on the concepts of probabilistic inference. The network can identify the relevant attributes for classification using a statistical technique called residual analysis. Using the information theoretic weight of evidence measure, the weighted connections are established between the processing elements representing the important attribute values and classes. The proposed network is non-iterative and is therefore very efficient computationally. Since the topology of the network is deterministic, the heuristic functions of each element can be precisely understood and the internal associations directly analyzed to synthesize explicit and intuitive classification rules. This network has been shown previously to outperform the back propagation networks and ID3 in terms of computational efficiency and classification accuracy in certain types of supervised learning applications. Using a typical fault diagnosis task, we show in this paper that the proposed neural network can be used effectively to acquire knowledge for rule-based expert systems. Compared to other AI-based knowledge acquisition approaches using AQ and CN2 algorithms, our proposed approach has the fastest training time while producing the most effective classification rules.

Paper Details

Date Published: 16 September 1992
PDF: 12 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140072
Show Author Affiliations
Keith C. C. Chan, IBM Canada Lab. (Canada)
John Y. Ching, Univ. of Waterloo (Canada)
Andrew K. C. Wong, Univ. of Waterloo (Canada)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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