基于权重修正和证据理论的信息融合方法

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Science Pub Date : 2024-10-28 DOI:10.1016/j.jocs.2024.102456
Xugang Xi , Yaqing Nie , Yu Zhou , Yun-Bo Zhao , Ting Wang , Yahong Chen , Lihua Li , Jian Yang
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引用次数: 0

摘要

对于来自不同信息源的高度冲突证据,Dempster-Shafer 证据理论的组合规则会导致不合逻辑的结果。我们从修改证据来源的角度出发,提出了冲突度计算的一般公式,即冲突系数和儒塞姆距离的加权和,并通过自适应地调整这两个指标的比例来定义新的焦点要素离散度指标:如果焦点要素离散度过高,冲突系数的影响就会增大,反之亦然。然后,我们定义了偏好一致性的概念,并提出了计算这一指标的公式,该公式可根据所有证据的偏好重新分配单个证据的权重。最后,典型的例子表明,所提出的规则能以更好的收敛性和抗干扰性管理冲突证据。
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An information fusion approach based on weight correction and evidence theory
The combination rules of the Dempster-Shafer evidence theory can lead to illogical results for highly conflicting evidence from different information sources. We propose a general formula for conflict degree calculation from the perspective of modifying evidence sources as a weighted sum of conflict coefficients and Jousselme distance, and the new metric of focal element dispersion is defined by adaptively adjusting the ratio of these two metrics: if the focal element dispersion is too high, the impact of conflict coefficients is increased, and vice versa. We then define the concept of preference consistency and propose a formula for calculating this metric that redistributes the weights of individual pieces of evidence based on the preferences of all evidence. Finally, typical examples show that the proposed rules can manage conflicting evidence with better convergence and interference resistance.
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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