【作者】 李理； 谢永芳； 陈晓方；

【Author】 LI Li;XIE Yong-fang;CHEN Xiao-fang;School of Automation, Central South University;Peng Cheng Laboratory;

【机构】 中南大学自动化学院； 鹏程实验室；

【摘要】 溯因推理过程是经验知识、工艺知识和机理知识等知识融合与处理的过程,由于实际生产过程中各类知识存在不确定性和模糊性,使用传统的模糊值形式表达模糊信息缺乏的高可靠性和高准确性。因此,文章提出基于区间云模型的动态不确定因果图模型。该模型是一种基于图形的知识表示和推理模型,它通过结合区间云模型与动态不确定因果图(DUCG)模型,能够解决传统动态不确定因果图模型难以对模糊事件变量进行准确描述的问题。该模型通过区间云模型处理不确定信息的模糊性和随机性,提高对不确定性知识表示和推理的能力。通过实际工业应用证明了该方法的有效性,而且,该方法较已有的动态不确定因果图方法具有更高的可靠性。更多还原

【Abstract】 The cause reasoning in the industrial production is a process of fusing and processing knowledge such as experienced knowledge, process knowledge and mechanism knowledge. Due to the fuzziness and ambiguity of knowledge in the actual production, it is difficult to ensure the high reliability and accuracy of the cause reasoning by adopting the traditional fuzzy values to describe uncertain information. Therefore, this paper proposes a dynamic uncertain causality graph model based on interval-valued cloud theory, which is a graph-based uncertain knowledge representation and reasoning model. It combines the interval-valued cloud theory with the dynamic uncertain causality graph(DUCG) model to deal with the problem that the traditional DUCG cannot precisely describe the uncertain knowledge. The model describes uncertain knowledge by using interval-valued cloud theory to handle with the fuzziness and randomness of uncertain information simultaneously, which can improve the ability of uncertain representation and reasoning. Then an application of the cause analysis proves the effectiveness of the method, and the results show that the proposed model is more reliable than the existing DUCG model.更多还原