Information fusion is an important research direction. In this field, there are plenty of ways to combine evidence. Initially, Yager proposed a soft‐likelihood function based on the ordered weighted average (OWA) operator to effectively fuse compatible probabilistic evidence. Recently, Song et al proposed a new soft‐likelihood function based on the power ordered weighted average (POWA) operator. However, through analysis, we find Song et al's method has the following two shortcomings: (a) The weight of POWA cannot comprehensively reflect the relation between probability and OWA operator. (b) The soft‐likelihood function does not reflect the preferences of decision makers. To overcome the above problem, we propose a modified soft‐likelihood function. The effectiveness of the proposed method is demonstrated from the perspective of theoretical analysis and numerical examples.

中文翻译：

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一种基于POWA算子的修正软似然函数

信息融合是一个重要的研究方向。在这个领域，有很多方法可以结合证据。最初，Yager 提出了一种基于有序加权平均 (OWA) 算子的软似然函数，以有效地融合兼容的概率证据。最近，Song 等人提出了一种新的基于幂次加权平均 (POWA) 算子的软似然函数。但是，通过分析，我们发现宋等人的方法有以下两个不足： (a) POWA 的权重不能全面反映概率与 OWA 算子之间的关系。(b) 软似然函数不反映决策者的偏好。为了克服上述问题，我们提出了一种改进的软似然函数。