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- W2621114456 abstract "Linguistic rule bases provide an alternative or complement to analysis where traditional mathematical models do not exist and human expertise is widely available. A linguistic term inherited three types of inexactness (generality, ambiguity and vagueness) which can be dealt with by membership functions. This paper discusses imprecision of human knowledge and the process of membership elicitation to establish linguistic rules. A Random-Sampling-Without-Replacement technique is proposed to maximize the efficiency of knowledge elicitation from human experts. A fuzzy rule network (FRN) structure for representing linguistic knowledge is implemented, and a method for adaptive rule calibrations is presented to maximize overall rulebase performance." @default.
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- W2621114456 date "1995-06-01" @default.
- W2621114456 modified "2023-09-30" @default.
- W2621114456 title "Linguistic Knowledge and Fuzzy Membership Elicitation" @default.
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- W2621114456 doi "https://doi.org/10.1016/s1474-6670(17)45208-6" @default.
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