广西师范大学学报(自然科学版) ›› 2015, Vol. 33 ›› Issue (1): 45-51.doi: 10.16088/j.issn.1001-6600.2015.01.008

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基于证据距离和模糊熵的加权证据融合新方法

侯晓东, 蔡斌斌, 金炜东, 段旺旺   

  1. 西南交通大学电气工程学院,四川成都610031
  • 收稿日期:2014-10-28 出版日期:2015-03-15 发布日期:2018-09-17
  • 通讯作者: 金炜东(1959—),男,四川成都人,西南交通大学教授,博导.E-mail:1071847374@qq.com
  • 基金资助:
    国家自然科学基金重点项目(61134002)

A New Weighted Evidence Fusion Algorithm Based on Evidence Distanceand Fuzzy Entropy Theory

HOU Xiao-dong, CAI Bin-bin, JIN Wei-dong, DUAN Wang-wang   

  1. School of Electrical Engineering,Southwest Jiaotong University,Chengdu Sichuan 610031, China
  • Received:2014-10-28 Online:2015-03-15 Published:2018-09-17

摘要: 证据的不确定性从根本上影响到融合结果,目前证据理论中还没有完善的不确定性度量方法。针对D-S证据理论在合成高冲突证据时会得到有悖常理的结果的问题,许多学者提出了修正证据源的改进方法,但是这些方法大多没有考虑到证据的不确定性问题。模糊熵方法是一种非常有效的模糊性(不确定性)的测度方法。考虑到证据的不确定性,本文提出一种新的基于证据距离和模糊熵的加权证据融合方法,该方法利用模糊熵方法计算各个证据的不确定度系数,修正基于证据距离的各证据源的权重,得到各证据源的综合权重。实验结果证明了本文方法的有效性。

关键词: 证据理论, 模糊熵, Jousselme距离, 证据冲突

Abstract: The evidence ambiguity can fundamentally affect the fusion results, but no impeccable method has been found to measure the evidence ambiguity in evidence theory. To suppress the counterintuitive results generated in the combination of high conflicting evidences, many scholars have proposed modified combination approaches based on the correction of original evidence. However, these methods do not take evidence ambiguity into consideration. Fuzzy entropy method can effectively evaluate the ambiguity (uncertainty). Given the evidence uncertainty, this paper proposes a new weighted evidence fusion algorithm based on evidence distance and fuzzy entropy theory. The coefficients of uncertainty of each evidence can be obtained by the fuzzy entropy method. Then, the weights acquired by evidence distance are amended by the coefficients of uncertainty to obtain the synthesis weights. The obtained results show the effective results of the proposed method.

Key words: evidence theory, fuzzy entropy, Jousselme distance, conflict of evidence

中图分类号: 

  • TP391
[1] YAGER R R. Comparing approximate reasoning and probabilistic reasoning using the Dempster-Shafer framework[J]. International Journal of Approximate Reasoning, 2009, 50(5): 812-821.
[2] 李鹏,刘思峰. 基于灰色关联分析和D-S证据理论的区间直觉模糊决策方法[J]. 自动化学报, 2011, 37(8):993-998.
[3] 周哲,许晓彬,文成林,等. 冲突证据融合的优化方法[J]. 自动化学报,2012,38(6):976-985.
[4] 杨风暴,王肖霞. D-S证据理论的冲突证据合成方法[M]. 北京:国防工业出版社, 2010:139-221.
[5] HAENNI R. Are alternatives to Dempster’s rule of combination real alternative?: Comments on “About the belief function combination and the conflict management problem” -Lefever et al[J].Information Fusion, 2002, 3(3): 237-239.
[6] MURPHY C K. Combining belief functions when evidence conflicts[J]. Decision Support Systems, 2000, 29(1): 1-9.
[7] DENG Yong,SHI Wen-kang,ZHU Zhen-fu, et al. Combining belief functions based on distance of evidence[J]. Decision Support Systems, 2004, 38(3): 489-493.
[8] JOUSSELME A L,LIU Chun-sheng, GRENIER D,et al. Measuring ambiguity in the evidence theory[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 2006,36(5):890-903.
[9] 王连锋,宋建社,朱昱,等. 基于模糊聚类分析的证据组合[J]. 系统工程与电子技术,2013,35(1):113-119.
[10] 李文立,郭凯红.D-S证据理论合成规则及冲突问题[J].系统工程理论与实践,2010,30(8):1422-1432.
[11] LIU Wei-ru. Analyzing the degree of conflict among belief functions[J]. Artificial Intelligence, 2006,170(11):909-924.
[12] JOUSSELME A L,GRENIER D,BOSSE E. A new distance between two bodies of evidence[J]. Information Fusion, 2001, 2(2):91-101.
[13] 吕峰,杜妮,文成林. 一种模糊-证据kNN分类方法[J].电子学报,2012,40(12):2390-2395.
[14] 韩德强,韩崇昭,邓勇,等.基于证据方差的加权证据组合[J].电子学报,2011,39(S1): 153-157.
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