Abstract
This paper presents a fuzzy knowledge representation, acquisition and reasoning scheme suitable for diagnostic systems. In addition to fuzzy sets and fuzzy production rules, we propose to using proximity relations for representing the interrelationship between symptoms in the antecedence of fuzzy production rules. A systematic generation method for acquiring proximity relations is proposed. An approximate reasoning algorithm based on such representation is also shown. Application to vibration cause identification in rotating machines is illustrated. Our scheme subsumes other fuzzy set based knowledge representation and reasoning approaches when proximity relation is reduced to identity relation.
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CITATION STYLE
Wang, S. L., & Wu, Y. H. (1999). A fuzzy knowledge representation and acquisition scheme for diagnostic systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1611, pp. 13–22). Springer Verlag. https://doi.org/10.1007/978-3-540-48765-4_4
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