Concurrent events risk assessment generic models with enhanced reliability using Fault tree analysis and expanded rotational fuzzy sets

Mahmood, Nabeel; Butalia, Tarunjit S.; Qin, Rongjun; Manasrah, Maram

doi:10.1016/j.eswa.2022.116681

Cited by 9 publications

(1 citation statement)

References 42 publications

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“…Uncertain information [ 1 ] plays a significant role in many engineering applications, including multi-attribute decision-making [ 2 , 3 , 4 ], fault diagnosis [ 5 , 6 , 7 ], image processing [ 8 , 9 ], knowledge inference [ 10 , 11 ], risk assessment [ 12 , 13 , 14 , 15 ], and pattern classification [ 16 , 17 ]. Recent studies have focused on how to measure and handle information uncertainty, and a series of theories have been introduced, such as modeling information uncertainty based on the entropy function [ 18 , 19 , 20 , 21 ] and using Dempster–Shafer (DS) evidence theory [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ] to deal with uncertain information.…”

Section: Introductionmentioning

confidence: 99%

A Decision Probability Transformation Method Based on the Neural Network

Zhao

Liu

2022

Entropy

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When the Dempster–Shafer evidence theory is applied to the field of information fusion, how to reasonably transform the basic probability assignment (BPA) into probability to improve decision-making efficiency has been a key challenge. To address this challenge, this paper proposes an efficient probability transformation method based on neural network to achieve the transformation from the BPA to the probabilistic decision. First, a neural network is constructed based on the BPA of propositions in the mass function. Next, the average information content and the interval information content are used to quantify the information contained in each proposition subset and combined to construct the weighting function with parameter r. Then, the BPA of the input layer and the bias units are allocated to the proposition subset in each hidden layer according to the weight factors until the probability of each single-element proposition with the variable is output. Finally, the parameter r and the optimal transform results are obtained under the premise of maximizing the probabilistic information content. The proposed method satisfies the consistency of the upper and lower boundaries of each proposition. Extensive examples and a practical application show that, compared with the other methods, the proposed method not only has higher applicability, but also has lower uncertainty regarding the transformation result information.

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