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 refined expected value decision rules on the issues of decision making that is described in the orthopair fuzzy environment. Numerical examples were applied to verify the availability and flexibility of the new refined expected value decision rules model. The experimental results demonstrate that the proposed model can apply refined expected value decision rules in the orthopair fuzzy environment and solve the decision making issues with the orthopair fuzzy environment successfully.

A Fault Analysis Method for Three‐Phase Induction Motors Based on Spiking Neural P Systems

“…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].…”

“…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 −…”

“…e fault prediction of a motor is usually realized based on an online monitoring system to detect the early failure symptoms and trend parameters that can reflect hidden troubles. en, these symptoms and parameters are processed by prediction algorithms to obtain early-warning information and integrated decision making [6] to prevent motor failures. For example, [7] diagnosed mechanical faults of motors by vibration analysis, which was carried out through a noncontact approach based on an optical computer mouse and a digital signal processing device.…”

Probability Transform Based on the Ordered Weighted Averaging and Entropy Difference

“…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].…”

FR–KDE: A Hybrid Fuzzy Rule-Based Information Fusion Method with its Application in Biomedical Classification

“…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].…”