A Novel Conflict Evidence Combination Method Based on Proof by Contradiction and Complete Frame of Discernment

Author(s):  
Shuang Yu ◽  
Xin Wang
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Bin Wu ◽  
Xiao Yi

Conflict evidence combination is an important research topic in evidence theory. In this paper, two kinds of transition matrices are constructed based on the Markov model; one is the unordered transition matrix, which satisfies the commutative law, and the other is the temporal transition matrix, which does not satisfy the commutative law, but it can handle the combination of temporal evidence well. Then, a temporal conflict evidence combination model is proposed based on these two transition matrices. First, the transition probability at the first n time is calculated through the model of unordered transition probability, and then, the transition matrix from the N + 1 time is used to solve the combination problem of temporal conflict evidence. The effectiveness of the transition matrix in the research of conflict evidence combination method is proved by the example analysis.


2014 ◽  
Vol 536-537 ◽  
pp. 443-449
Author(s):  
Dong Ying Bai ◽  
Jun Han ◽  
Jian Wang ◽  
Song Li

Aiming at the paradox of D-S evidence theory and computations exponential growth in dealing with large scale conflict evidence combination, a new weighted evidence combination method was proposed, which used conflict coefficient and evidence distance in order to measure the conflict. Through the analysis of single conflict representations weaknesses, compound conflict coefficient has been put forward, meanwhile, the evidence center and current center distance were defined, evidence weight was determined with current center distance and conflict coefficient. The experiment results show that the algorithm settles the paradox effectively, at the same time, computing speed has been greatly enhanced.


Author(s):  
Xiaochen Xing ◽  
Yuanwen Cai ◽  
Zhengyu Zhao ◽  
Long Cheng ◽  
Yan Li

Author(s):  
Haobin Shi ◽  
Shuyun Yang ◽  
Zhenliang Cao ◽  
Wei Pan ◽  
Weihua Li

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