Interval-valued belief entropies for Dempster–Shafer structures

被引:0
作者
Yige Xue
Yong Deng
机构
[1] University of Electronic Science and Technology of China,Institute of Fundamental and Frontier Sciences
[2] Shaanxi Normal University,School of Education
[3] Japan Advanced Institute of Science and Technology,School of Knowledge Science
来源
Soft Computing | 2021年 / 25卷
关键词
Dempster–Shafer structures; Belief entropy; Uncertainty; Shannon entropy; Interval-valued entropies;
D O I
暂无
中图分类号
学科分类号
摘要
In practical application problems, the uncertainty of an unknown object is often very difficult to accurately determine, so Yager proposed the interval-valued entropies for Dempster–Shafer structures, which is based on Dempster–Shafer structures and classic Shannon entropy and is an interval entropy model. Based on Dempster–Shafer structures and classic Shannon entropy, the interval uncertainty of an unknown object is determined, which provides reference for theoretical research and provides help for industrial applications. Although the interval-valued entropies for Dempster–Shafer structures can solve the uncertainty interval of an object very efficiently, its application scope is only a traditional probability space. How to extend it to the evidential environment is still an open issue. This paper proposes interval-valued belief entropies for Dempster–Shafer structures, which is an extension of the interval-valued entropies for Dempster–Shafer structures. When the belief entropy degenerates to the classic Shannon entropy, the interval-valued belief entropies for Dempster–Shafer structures will degenerate into the interval-valued entropies for Dempster–Shafer structures. Numerical examples are applied to verify the validity of the interval-valued belief entropies for Dempster–Shafer structures. The experimental results demonstrate that the proposed entropy can obtain the interval uncertainty value of a given uncertain object successfully and make decision effectively.
引用
收藏
页码:8063 / 8071
页数:8
相关论文
共 140 条
[1]  
Abellán J(2017)Analyzing properties of deng entropy in the theory of evidence Chaos Solitons Fract 95 195-199
[2]  
Abellan J(2018)Drawbacks of uncertainty measures based on the pignistic transformation IEEE Trans Syst Man Cybern Syst 48 382-388
[3]  
Bosse E(2019)Entropic divergence and entropy related to nonlinear master equations Entropy 21 993-3316
[4]  
András T(2019)Entropic divergence and entropy related to nonlinear master equations Entropy 63 210201-1402
[5]  
Sándor BT(2020)Uncertainty measure in evidence theory Sci China Inf Sci 16 4106-84
[6]  
Zoltán N(2021)Information volume of fuzzy membership function Int J Comput Commun Control 34 3302-11
[7]  
Biró TS(2019)A total uncertainty measure for D numbers based on belief intervals Int J Intell Syst 21 1389-428
[8]  
Néda Z(2019)Evaluating green supply chain management practices under fuzzy environment: a novel method based on D number theory Int J Fuzzy Syst 105 66-45
[9]  
Telcs A(2020)Multisensor fusion method based on the belief entropy and ds evidence theory J Sensors 35 1-1347
[10]  
Deng Y(2019)Selecting strategic partner for tax information systems based on weight learning with belief structures Int J Approx Reason 347 417-65