李姝昊,王晓丹,宋亚飞.基于皮尔逊系数和不确定测度的冲突证据组合方法[J].电子测量与仪器学报,2021,35(8):38-45
基于皮尔逊系数和不确定测度的冲突证据组合方法
Conflict evidence combination method based on Pearsoncoefficient and uncertainty measure
  
DOI:
中文关键词:  D-S 证据理论  证据冲突  皮尔逊相关系数  可信度  不确定测度
英文关键词:D-S evidence theory  evidence conflict  Pearson correlation coefficient  credibility  uncertainty measure
基金项目:国家自然科学基金(61876189,61273275,61703426,61806219)、陕西省高校科协青年人才托举计划(2019038)、陕西省创新能力支撑计划(2019 065)项目资助
作者单位
李姝昊 1.空军工程大学 防空反导学院 
王晓丹 1.空军工程大学 防空反导学院 
宋亚飞 1.空军工程大学 防空反导学院 
AuthorInstitution
Li Shuhao 1.Air and Missile Defense College, Air Force Engineering University 
Wang Xiaodan 1.Air and Missile Defense College, Air Force Engineering University 
Song Yafei 1.Air and Missile Defense College, Air Force Engineering University 
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中文摘要:
      D-S 证据理论在合成冲突较大的证据时会产生直觉相悖问题,现有的修正证据源的改进方法大多只从单一角度做出改 进,不能全面反映冲突信息特征。 针对此问题,提出了一种基于皮尔逊相关系数和不确定性测度的证据组合方法。 首先,利用 皮尔逊相关系数衡量证据之间的相关性,定义证据的可信度。 其次,引入基于区间概率的不确定度对可信度进行修正,得到权 重。 最后,使用该权重对原始证据进行加权平均,使用 Dempster 组合规则进行合成。 与经典改进方法相比,该方法能有效处理 冲突证据的融合问题,指认正确命题的准确率达到 0. 992 0。 与已有皮尔逊系数改进方法相比该方法更具有合理性,且有较高 的准确度。
英文摘要:
      D-S evidence theory will produce intuitionistic conflict when synthesizing evidence with large conflict. Since most of the existing improvement methods for correcting evidence sources only make improvements from a single perspective, they cannot fully reflect the characteristics of conflict information. In order to solve this problem, a new evidence combination method based on Pearson correlation coefficient and uncertainty is proposed. Firstly, using the Pearson correlation coefficient to measure the correlation between evidences, to define the credibility of the evidence. Secondly, the uncertainty based on interval probability is introduced to modify the credibility to obtain the weight. Finally, using the weight of the weighted average of the original evidence, synthesized using Dempster combination rule. In the case analysis, compared with the classic improved method, the proposed method can effectively deal with the fusion of conflicting evidence, and the accuracy of identifying the correct proposition reaches 0. 992 0. Compared with the existing Pearson coefficient improvement method, the proposed method is more reasonable and has higher accuracy.
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