A new method of determining the basic belief assignment in D-S evidence theory

Author(s):  
Yong Hu ◽  
Jun Gao ◽  
Liang-Mei Hu ◽  
Huo-Ming Dong
Author(s):  
Lipeng Pan ◽  
Yong Deng

Dempster-Shafer evidence theory can handle imprecise and unknown information, which has attracted many people. In most cases, the mass function can be translated into the probability, which is useful to expand the applications of the D-S evidence theory. However, how to reasonably transfer the mass function to the probability distribution is still an open issue. Hence, the paper proposed a new probability transform method based on the ordered weighted averaging and entropy difference. The new method calculates weights by ordered weighted averaging, and adds entropy difference as one of the measurement indicators. Then achieved the transformation of the minimum entropy difference by adjusting the parameter r of the weight function. Finally, some numerical examples are given to prove that new method is more reasonable and effective.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 15547-15555 ◽  
Author(s):  
Renliang Sun ◽  
Yong Deng
Keyword(s):  

2011 ◽  
Vol 6 (6) ◽  
Author(s):  
Wen Jiang ◽  
Yong Deng ◽  
JinYe Peng
Keyword(s):  

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 381 ◽  
Author(s):  
Zhan Deng ◽  
Jianyu Wang

As an important method for uncertainty modeling, Dempster–Shafer (DS) evidence theory has been widely used in practical applications. However, the results turned out to be almost counter-intuitive when fusing the different sources of highly conflicting evidence with Dempster’s combination rule. In previous researches, most of them were mainly dependent on the conflict measurement method between the evidence represented by the evidence distance. However, it is inaccurate to characterize the evidence conflict only through the evidence distance. To address this issue, we comprehensively consider the impacts of the evidence distance and evidence angle on conflicts in this paper, and propose a new method based on the mutual support degree between the evidence to characterize the evidence conflict. First, the Hellinger distance measurement method is proposed to measure the distance between the evidence, and the sine value of the Pignistic vector angle is used to characterize the angle between the evidence. The evidence distance indicates the dissimilarity between the evidence, and the evidence angle represents the inconsistency between the evidence. Next, two methods are combined to get a new method for measuring the mutual support degree between the evidence. Afterward, the weight of each evidence is determined by using the mutual support degree between the evidence. Then, the weights of each evidence are utilized to modify the original evidence to achieve the weighted average evidence. Finally, Dempster’s combination rule is used for fusion. Some numerical examples are given to illustrate the effectiveness and reasonability for the proposed method.


2011 ◽  
Vol 204-210 ◽  
pp. 2061-2064
Author(s):  
Fang Wei Zhang ◽  
Shi He Xu ◽  
Bao Shi

In this paper we study the multi-attribute group decision-making problems and put forward a kind of method. In this method, based on clustering evidence theory, the decision-making information is translated into evidences to support different decision-making program. Then, by the amount of evidences, decision-making program ranking is completed. The method’s character can not only rank the decision-making programs by their merits, but also give each program the probability to be the best. Finally, an example is given to show the rationality and effectiveness of the new method.


Sign in / Sign up

Export Citation Format

Share Document