贝叶斯网络是在不确定性环境下有效的知识表示方式和概率推理模型，是一种流行的图形决策化分析工具。
Bayesian Networks is a model that efficiently represents knowledge and probabilistic inference and is a popular graphics decision-making analysis tool.
本文论述了模糊子集的云模型表示、基于云模型控制规则的不确定性推理，并设计了一种二维云模型控制器。
This paper presents the cloud model of fuzzy sets, uncertainty reasoning of control rules based on cloud model, and a two-dimension cloud model controller.
讨论了一种概率信息系统用于表示对象与属性值之间的关系存在不确定性的信息。
Probability information systems, which are used to represent the uncertainty information between objects and attributes, are discussed.
本文提出了一种多特征融合技术，将某一像素所属的区域用隶属度表示出来，很好地适应了这种物体边界的不确定性。
A multi-feature fusion approach is proposed to express which region one pixel belongs to, and it is suitable for the uncertainty.
本文提出了一种多特征融合技术，将某一像素所属的区域用隶属度表示出来，很好地适应了这种物体边界的不确定性。
A multi-feature fusion approach is proposed to express which region one pixel belongs to, and it is suitable for the uncertainty.
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