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YIN D L , HUANG X Y , WU Y J , et al . Research on target identification decision method based on cloud model and improved D-S evidence theory [J ] . Acta Aeronautica et Astronautica Sinica , 2021 , 42 ( 12 ): 299 - 310 . (in Chinese)

吴斌 , 衣晓 , 李双明 . 一种区间距离的冲突证据组合方法 [J ] . 兵工学报 , 2020 , 41 ( 6 ): 1140 - 1150 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2020.06.010 http://doi.org/10.3969/j.issn.1000-1093.2020.06.010 针对含有冲突证据的多条证据组合结果出现反直观的不足，提出基于不确定区间距离的冲突证据组合方法。通过文献［13，15，17］分析发现，由信度及似真函数构成的区间包含事件本身所有的信息，解决了直接由Mass函数构成证据组合过程中信息缺失的问题。根据不确定区间定义了一种区间距离。通过区间距离生成证据间的距离矩阵以及证据间的权重，利用权重对原有证据进行修正，基于Dempster-Shafer证据理论及第6种比例冲突再分配完成证据组合。算例分析结果表明：新方法在冲突度表征变化上与文献［33］的变化趋势一致，证明了该方法的有效性；在多条冲突证据的组合中，新方法的信度值比现有冲突度量方法明显提高，证明了该方法的正确性。

WU B , YI X , LI S M . Conflict evidence combination method based on interval distance [J ] . Acta Armamentarii , 2020 , 41 ( 6 ): 1140 - 1150 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2020.06.010 http://doi.org/10.3969/j.issn.1000-1093.2020.06.010 A conflict evidence combination method based on uncertain interval distance is proposed for the anti-intuitive problem about combination results of multiple pieces of evidences containing conflict evidence. Through the analysics in Refs. ［13, 15, 17］, it is found that the interval composed of Bel and Pl function contains all the information of an event, which solves the problem of information missing in the process of evidence combination directly composed of Mass function. The expression of uncertain interval is given, and then an interval distance is defined according to the uncertain interval. The interval distances among the same focal elements of different evidences are calculated, and the interval distances of all focal elements are summed. A distance matrix and weights among evidences are generated through the distance matrix, and the original evidences are corrected according to the weights. The evidence combination is completed with Dempster-Shafer(D-S) and Proportional Conflict Redistribution 6 (PCR 6). The example analysis shows that the proposed method agrees with the methods in Ref. ［33］ in the representation change of conflict degree, which shows the effectiveness of the proposed method. In the combination of multiple conflict evidences, the Bel of the proposed method is significantly improved compared with the existing methods. Key

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MA W J , JIANG Y C , LUO X D . A flexible rule for evidential combination in Dempster-Shafer theory of evidence [J ] . Applied Soft Computing , 2019 , 85 : 105512 . DOI: 10.1016/j.asoc.2019.105512 http://doi.org/10.1016/j.asoc.2019.105512 https://linkinghub.elsevier.com/retrieve/pii/S1568494619302820 https://linkinghub.elsevier.com/retrieve/pii/S1568494619302820

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ZHANG C F , YANG H W , HU W D , et al . New rule of evidence combination based on improved conflict measure [J ] . Systems Engineering and Electronics , 2009 , 31 ( 11 ): 2637 - 2640 ,2727. (in Chinese) To solve the problem that the DS combination rule gives a conservative estimate to the conflict measure between evidences, a new evidence combination rule is proposed. All the potential conflicts are fully considered in the new combination rule, which ensures that the inconsistent information is suppressed and the consistent information is magnified. The combined evidence gotten with the new combination rule has a better convergence characteristic than that gotten with the DS combination rule, which is helpful to the subsequent decision for target identification. Simulation experiments verify the effectiveness of the new combination rule.

孙贵东 , 关欣 , 衣晓 , 等 . 互补证据组合规则 [J ] . 电子学报 , 2018 , 46 ( 11 ): 2738 - 2745 . DOI: 10.3969/j.issn.0372-2112.2018.11.022 http://doi.org/10.3969/j.issn.0372-2112.2018.11.022 针对Dempster组合规则"一票否决"和现有修正方法主观分配冲突信息的缺点，提出一种全新的证据组合方法.新方法直接利用定义的互补证据进行融合，不再采用Dempster组合规则中的纯乘性策略组合证据，成功解决了"一票否决"问题.并将冲突信息转化为证据权重融入组合规则进行直接融合，不再主观分配其信度.新的组合规则是一类广义的加性集成算子，既满足交换律又满足结合律，既能够直接组合单类证据，也能够基于TBM组合复合类证据.通过多传感器融合识别算例，验证和对比分析了新的组合规则的优势.

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TANG X H , GU X , WANG J C , et al . A bearing fault diagnosis method based on feature selection feedback network and improved DS evidence fusion [J ] . IEEE Access , 2020 , 8 : 20523 - 20536 . DOI: 10.1109/Access.6287639 http://doi.org/10.1109/Access.6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

ZHANG W Q , DENG Y . Combining conflicting evidence using the DEMATEL method [J ] . Soft Computing , 2019 , 23 : 8207 - 8216 . DOI: 10.1007/s00500-018-3455-8 http://doi.org/10.1007/s00500-018-3455-8 Dempster-Shafer evidence theory is widely used in the information fusion field for its effectivity in representing and handling uncertain information. However, applications of Dempster rule in combining multiple conflicting evidence often cause counterintuitive results. One of the existing researches on conflict is based on the similarity of evidence. However, due to the fact that computational complexity of the existing methods is large, it is difficult to meet the real-time requirements of systems. Therefore, new effective methods with acceptable expense should be explored. In this article, following the idea of modifying the source model of evidence, a new method based on DEMATEL is proposed to take the weight of each evidence into consideration. First, the total-relation matrix is determined by the similarity among evidence. Second, prominence and importance are calculated. Finally, the weighted average combination result can be obtained based on Dempster's rule of combination. Numerical examples are used to demonstrate that the proposed model is efficient to both deals with conflicting evidence and reduce computational complexity.

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LI L , ZHU F , SUN H , et al . Multi-source information fusion and deep-learning-based characteristics measurement for exploring the effects of peer engagement on stock price synchronicity [J ] . Information Fusion , 2021 , 69 : 1 - 21 . DOI: 10.1016/j.inffus.2020.11.006 http://doi.org/10.1016/j.inffus.2020.11.006 https://linkinghub.elsevier.com/retrieve/pii/S1566253520304127 https://linkinghub.elsevier.com/retrieve/pii/S1566253520304127