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Fuzzy neural network in case-based diagnostic system

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2 Author(s)
Zhi-Qiang Liu ; Dept. of Comput. Sci., Melbourne Univ., Parkville, Vic., Australia ; F. Yan

Diagnosing electronic systems for symptoms supplied by customers is often difficult as human descriptions of symptoms are for the most part uncertain and ambiguous. As a result, traditional expert systems are not effective in providing reliable analysis, often require a large set of rules, and lack flexibility in terms of learning and modification. In this paper, we propose a fuzzy logic-based neural network (FLBN) to develop a case-based system for diagnosing symptoms in electronic systems. We demonstrate through data obtained from a real call-log database that the FLBN is able to perform fuzzy AND/OR logic rules and to learn from samples. Such a system is simple to develop and can achieve the performance similar to that of the human expert

Published in:

IEEE Transactions on Fuzzy Systems  (Volume:5 ,  Issue: 2 )