Dempster–Shafer evidence theory has been widely used in various fields of applications, because of the flexibility and effectiveness in modeling uncertainties without prior information. However, the existing evidence theory is insufficient to consider the situations where it has no capability to express the fluctuations of data at a given phase of time during their execution, and the uncertainty and imprecision which are inevitably involved in the data occur concurrently with changes to the phase or periodicity of the data. In this paper, therefore, a generalized Dempster–Shafer evidence theory is proposed. To be specific, a mass function in the generalized Dempster–Shafer evidence theory is modeled by a complex number, called as a complex basic belief assignment, which has more powerful ability to express uncertain information. Based on that, a generalized Dempster’s combination rule is exploited. In contrast to the classical Dempster’s combination rule, the condition in terms of the conflict coefficient between the evidences

Dempster–Shafer证据理论由于在无需先验信息的情况下对不确定性进行建模的灵活性和有效性而被广泛应用于各个应用领域。但是，现有证据理论不足以考虑这样一种情况，即在执行过程中，它无法表达给定时间段内数据的波动，并且不可避免地涉及数据的不确定性和不精确性会随着数据变化而同时发生。数据的相位或周期性。因此，本文提出了广义的Dempster-Shafer证据理论。具体而言，广义Dempster-Shafer证据理论中的质量函数由复数建模，称为复数基本信念分配，它具有更强的表达不确定信息的能力。基于此，利用了通用的Dempster组合规则。与经典的登普斯特组合规则相反，根据证据之间的冲突系数得出的条件