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The fuzzy inference system (FIS) is useful for developing an improved Risk Priority Number (RPN) model for risk evaluation in failure mode and effect analysis (FMEA). A general FIS_RPN model considers three risk factors, i.e., severity, occurrence, and detection, as the inputs and produces an FIS_RPN score as the output. At present, there are two issues pertaining to practical implementation of classical...
The use of Fuzzy Inference System (FIS) in decision making problems has received little attention so far. This may be due to the difficulty in gathering a complete set of fuzzy rules, which is free from noise, and the complexity in constructing an FIS model that is able to satisfy a number of important properties, including the monotonicity property. Previously, we have proposed a single-input Monotone-Interval...
The focus of this paper is on handling non-monotone information in the modelling process of a single-input target monotone system. On one hand, the monotonicity property is a piece of useful prior (or additional) information which can be exploited for modelling of a monotone target system. On the other hand, it is difficult to model a monotone system if the available information is not monotonically-ordered...
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