对信念函数和它们在数据融合中的应用进行新的相关性测量
Zhuo Zhang1, Hongfei Wang1, Jianting Zhang2
1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
这项研究引入了一种新的信念相关性测量方法,将不确定性纳入Dempster-Shafer理论,以更好地处理信息. 新措施通过考虑证据的可信性和可用性来增强多来源数据的融合.
科学领域:
- 决策理论 决策理论
- 信息融合是一个信息融合.
- 不确定性量化不确定性的量化.
背景情况:
- 相关性测量在Dempster-Shafer理论中对于不确定的信息处理至关重要.
- 现有的相关性指标往往忽略了信息不确定性的影响.
- 需要一个全面的方法来量化考虑不确定性的信念函数之间的相关性.
研究的目的:
- 为信念函数提出一个新的相关性测量方法,该测量方法包含信息不确定性.
- 开发一种基于新型相关性测量方法的信息融合方法.
- 提高多源数据融合的准确性和全面性.
主要方法:
- 开发了一种信仰相关性测量方法,利用信仰和相对.
- 确保测量具有关键的数学属性:概率一致性,非消极性,非退化性,边界性,直角性和对称性.
- 提出了一种信息融合方法,将客观和主观权重纳入证据评估中.
主要成果:
- 建议的信念相关性衡量有效量化了信念函数之间的相关性,同时考虑了不确定性.
- 相关的信息融合方法为证据的可信性和可用性提供了更全面的评估.
- 数字示例和应用案例证明了该方法在多源数据融合中的有效性.
结论:
- 新的信念关联度提供了一个更全面的方法来量化信念函数之间的关系,包括不确定性.
- 拟议的信息融合方法通过更好地评估证据,改善了多源数据的融合.
- 这项工作推进了Dempster-Shafer理论在不确定信息处理和数据融合中的应用.
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