For the sake of great ability of handling uncertain information, Dempster‐Shafer evidence theory is extensively used in information fusion. Nevertheless, when there exists highly inconsistent evidences, using classical Dempster's combination rule may lead to counter‐intuitive results. To address this issue, a new conflicting evidences combination method based on distance function and Tsallis entropy is proposed. Numerical examples are used to illustrate the feasibility and efficiency of the proposed method. Further, an fault diagnosis problem is used as an example to show the effectiveness and superiority of the proposed method. The proposed method outperforms other methods that the proposed method recognize the target by the probability 99.49%, which is higher than other methods.

中文翻译：

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一种将冲突证据与改进的距离函数和 Tsallis 熵相结合的方法

为了处理不确定信息的能力很强，Dempster-Shafer证据理论被广泛用于信息融合。然而，当存在高度不一致的证据时，使用经典的登普斯特组合规则可能会导致违反直觉的结果。针对这一问题，提出了一种基于距离函数和Tsallis熵的新的冲突证据组合方法。数值例子被用来说明所提出方法的可行性和效率。此外，以故障诊断问题为例，说明了所提出方法的有效性和优越性。所提出的方法优于其他方法，所提出的方法识别目标的概率为99.49%，高于其他方法。