2020
DOI: 10.3390/math8030442
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Refined Expected Value Decision Rules under Orthopair Fuzzy Environment

Abstract: Refined expected value decision rules can refine the calculation of the expected value and make decisions by estimating the expected values of different alternatives, which use many theories, such as Choquet integral, PM function, measure and so on. However, the refined expected value decision rules have not been applied to the orthopair fuzzy environment yet. To address this issue, in this paper we propose the refined expected value decision rules under the orthopair fuzzy environment, which can apply the ref… Show more

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Cited by 15 publications
(9 citation statements)
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“…If a rule neuron contains a fault pulse, then the number of the neuron is numbered as 1; otherwise, it is 0. Fuse melt fault p 5 Damage of shaft seal ring structure p 6 Oil sealing material overheating p 7 Excessive roughness value of the seal surface shaft p 8 Excessive temperature p 9 Mechanical fault of the rotor winding p 10 e motor centerline is inconsistent with the pump one p 11 Fault of the bearing locking device p 12 Rotor core deformation p 13 Fracture or shedding of magnetic slot wedges p 14 Dewelding at the joint of the winding and lead wire p 15 Connection box joint loosened p 16 Poor contact of the power control loop switch p 17 Decrease in rotational speed p 18 Excessive current in a phase p 19 Excessive excitation current p 20 A e initial pulse value of input neurons and truth value of rule neurons are obtained via historical data and expert experience [23].…”
Section: Algorithm 3 Is Shown As Followsmentioning
confidence: 99%
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“…If a rule neuron contains a fault pulse, then the number of the neuron is numbered as 1; otherwise, it is 0. Fuse melt fault p 5 Damage of shaft seal ring structure p 6 Oil sealing material overheating p 7 Excessive roughness value of the seal surface shaft p 8 Excessive temperature p 9 Mechanical fault of the rotor winding p 10 e motor centerline is inconsistent with the pump one p 11 Fault of the bearing locking device p 12 Rotor core deformation p 13 Fracture or shedding of magnetic slot wedges p 14 Dewelding at the joint of the winding and lead wire p 15 Connection box joint loosened p 16 Poor contact of the power control loop switch p 17 Decrease in rotational speed p 18 Excessive current in a phase p 19 Excessive excitation current p 20 A e initial pulse value of input neurons and truth value of rule neurons are obtained via historical data and expert experience [23].…”
Section: Algorithm 3 Is Shown As Followsmentioning
confidence: 99%
“…if each rule neurons satisfies its firing condition E � a n ∧θ j ≥ λ r j , 1 ≤ j ≤ t then (4) rule neurons fire and compute N − (6) if each proposition neuron satisfies its firing condition E � a n ∧θ i ≥ λ p i , 1 ≤ i ≤ s then (7) proposition neurons fire and compute N −…”
Section: Forward Fault Predictionmentioning
confidence: 99%
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“…Such as Fuzzy sets [14,76], Intuitionistic fuzzy sets [4], Z number [30,77], Pythagorean fuzzy sets [72], and so on [24,27]. These theories are applied to decision-making [15,32,52,64], expert systems [41,48,49,66], pattern recognition [19,55,59,81]. On the other hand, some theories have been proposed based on the probabilistic of information, such as Probability theory [57,75], Dempster-Shafer (D-S) evidence theory [12,43], D number [9,10], Evidence reasoning [34,80], complex evidence theory [58,62,63], and so on [33,71,74].…”
Section: Introductionmentioning
confidence: 99%
“…Hence, information granulation that concentrates on representing, handling, and communicating information granules becomes the reason. Information granules are an essential concept in Granular computing and it is some abstract and semantically entities that can be expressed by some other theories, such as intervals [8][9][10], fuzzy sets [11], and evidence theory [12], etc. There are two major steps of GrC, the construction of granules and computing with granules [13].…”
Section: Introductionmentioning
confidence: 99%