2019
DOI: 10.1109/access.2019.2928051
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Linguistic Reasoning Petri Nets Using q-Rung Orthopair Fuzzy Linguistic Sets and Weighted Ordered Weighted Averaging Operators

Abstract: Fuzzy Petri nets (FPNs) are an important tool for knowledge representation and reasoning in the rule-based expert system. Recently, various fuzzy sets and linguistic models have been introduced into FPNs to improve its ability in handling imprecise, fuzzy, and linguistic information. However, the existing FPN models still have the following two deficiencies: The first one is the incompatibility of the knowledge representation parameters in modeling the membership degrees of linguistic variables and hesitancy o… Show more

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Cited by 7 publications
(3 citation statements)
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“…On the other side, in this study this evaluation has been performed under hesitancy. This stated approach contributes to achieving the most appropriate result when the experts do not agree with each other [73][74][75]. These experts consider five different linguistic evaluation scales, which are stated in Table 3.…”
Section: Discussionmentioning
confidence: 99%
“…On the other side, in this study this evaluation has been performed under hesitancy. This stated approach contributes to achieving the most appropriate result when the experts do not agree with each other [73][74][75]. These experts consider five different linguistic evaluation scales, which are stated in Table 3.…”
Section: Discussionmentioning
confidence: 99%
“…Generalized orthopair fuzzy sets have extended intuitionistic fuzzy sets [74] and Pythagorean fuzzy sets [75,76]. The orthopair fuzzy sets have advantages in representing uncertainties [77] and have been used in a wide scope of applications [78,79]. It is more flexible, practical and efficient than intuitionistic fuzzy sets and Pythagorean fuzzy sets in dealing with ambiguity and uncertainty [80,81].…”
Section: Generalized Orthopair Fuzzy Setsmentioning
confidence: 99%
“…The QROFS can take care of issues that the PFS and IFS cannot, for instance, if a DM problem gives the enrollment degree and the non-enrollment degree as 0.9 and 0.8, respectively, at that point it is just substantial for the QROFS. After the QROFS was effectively introduced, it got many advance studies [12][13][14]. Moreover, in our day-to-day life, vulnerability and ambiguity that are available in the information happen simultaneously with changes to the stage (periodicity) of the information.…”
Section: Introductionmentioning
confidence: 99%