北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (3): 616-623.doi: 10.13700/j.bh.1001-5965.2019.0264

• 论文 • 上一篇    下一篇

面向冲突证据的改进DS证据理论算法

张欢1, 陆见光1,2, 唐向红1,3   

  1. 1. 贵州大学 现代制造技术教育部重点实验室, 贵阳 550025;
    2. 公共大数据国家重点实验室, 贵阳 550025;
    3. 贵州大学 机械工程学院, 贵阳 550025
  • 收稿日期:2019-05-28 发布日期:2020-03-28
  • 通讯作者: 陆见光 E-mail:jglu@gzu.edu.cn
  • 作者简介:张欢,男,硕士研究生。主要研究方向:多源数据融合与故障诊断;陆见光,男,博士,副教授,硕士生导师。主要研究方向:机器学习与智能制造;唐向红,男,博士,教授,硕士生导师。主要研究方向:数据挖掘与故障诊断。
  • 基金资助:
    贵州省重大基础研究项目([2013]6019);贵州省留学回国人员科技活动择优资助项目(2018.0002);国家留学基金委项目(201806675013);贵州省公共大数据重点实验室开放基金(2017BDKFJJ019);贵州大学引进人才基金(贵大人基合字(2016)13号)

An improved DS evidence theory algorithm for conflict evidence

ZHANG Huan1, LU Jianguang1,2, TANG Xianghong1,3   

  1. 1. Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, China;
    2. State Key Laboratory of Public Big Data, Guiyang 550025, China;
    3. College of Mechanical Engineering, Guizhou University, Guiyang 550025, China
  • Received:2019-05-28 Published:2020-03-28
  • Supported by:
    Science and Technology Major Project of Guizhou Province ([2013]6019); Project of Guizhou High-Level Study Abroad Talents Innovation and Entrepreneurship (2018.0002); Project of China Scholarship Council (201806675013); Open Fund of Guizhou Provincial Public Big Data Key Laboratory (2017BDKFJJ019); Guizhou University Foundation for the Introduction of Talent ((2016) No. 13)

摘要: DS证据理论在处理不确定信息上的优势在各个领域得到广泛应用。针对传统DS(Dempster-Shafer)存在的证据冲突问题,提出了一种改进的DS证据理论算法。首先,结合皮尔逊相关系数的相关性限制和融合过程零因子的修正,较大程度上减少分配与整体非相关证据体的权值,修正证据体的整体重要程度;然后,按照修正后的基本概率分布(BPA)进行DS组合规则计算,得到融合结果。在解决常见冲突证据和证据体融合数量等方面与其他改进DS证据理论算法进行比较,所提算法收敛速度更快,融合的可信命题基本概率结果更高,因而验证了算法的有效性。

关键词: DS(Dempster-Shafer)证据理论, 证据冲突, 组合规则, 信息融合, 皮尔逊相关系数

Abstract: The advantages of DS (Dempster-Shafer) evidence theory in dealing with uncertain information have been widely used in various fields. This paper proposes an improved DS evidence theory algorithm for the existence of evidence conflicts in traditional DS. Firstly,combined with the correlation limitation of Pearson correlation coefficient and the correction of zero factor of fusion process, the weight of distribution and the overall unrelated evidence body is greatly reduced, and the overall importance of the evidence body is corrected. Secondly, the DS combination rule calculation is performed to corrected basic probability assignment (BPA) to obtain the fusion result. Compared with the performance of other improved DS theory algorithms in solving common conflict evidence and the number of evidence body fusion, the proposed algorithm has faster convergence rate and higher fusion BPA on credible proposition, which proves the effectiveness of the proposed algorithm.

Key words: DS (Dempster-Shafer) evidence theory, evidence conflict, combination rule, information fusion, Pearson correlation coefficient

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