Fuzzy neural network in case-based diagnostic system

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.