Yang Bin, Gan Dingyi, Tang Yongchuan, Lei Yan
School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China.
Entropy (Basel). 2020 Sep 7;22(9):993. doi: 10.3390/e22090993.
Quantifying uncertainty is a hot topic for uncertain information processing in the framework of evidence theory, but there is limited research on belief entropy in the open world assumption. In this paper, an uncertainty measurement method that is based on Deng entropy, named Open Deng entropy (ODE), is proposed. In the open world assumption, the frame of discernment (FOD) may be incomplete, and ODE can reasonably and effectively quantify uncertain incomplete information. On the basis of Deng entropy, the ODE adopts the mass value of the empty set, the cardinality of FOD, and the natural constant to construct a new uncertainty factor for modeling the uncertainty in the FOD. Numerical example shows that, in the closed world assumption, ODE can be degenerated to Deng entropy. An ODE-based information fusion method for sensor data fusion is proposed in uncertain environments. By applying it to the sensor data fusion experiment, the rationality and effectiveness of ODE and its application in uncertain information fusion are verified.
在证据理论框架下，量化不确定性是不确定信息处理的一个热门话题，但在开放世界假设下关于信度熵的研究却很有限。本文提出了一种基于邓熵的不确定性度量方法，称为开放邓熵（ODE）。在开放世界假设中，识别框架（FOD）可能不完整，而ODE能够合理有效地量化不确定的不完整信息。基于邓熵，ODE采用空集的质量值、FOD的基数和自然常数来构建一个新的不确定性因子，用于对FOD中的不确定性进行建模。数值例子表明，在封闭世界假设下，ODE可以退化为邓熵。提出了一种在不确定环境中基于ODE的传感器数据融合信息融合方法。通过将其应用于传感器数据融合实验，验证了ODE的合理性和有效性及其在不确定信息融合中的应用。
