摘要
在安全苛求系统中,潜在风险会引发灾难事故,研究分析潜在风险的影响至关重要.风险到事故的因果逻辑关系包括两类:确定性和非确定性的.确定性因果关系可以用事件树、故障树等分析.由于非确定性因果关系包含不确定性因素和数据不足,贝叶斯网络成为最佳选择.量化分析中,条件概率的分配是一件不容易的工作,本文提出一种基于模糊逻辑的分配方法,结合建立的不确定性影响模型,利用贝叶斯网络进行量化分析,分析确定性因素的影响.最后通过实例学习,验证和评估方法的有效性.
In the safety-critical systems, potential hazard may lead to catastrophic accidents, therefore it is greatly significant to analyze its influence. There are two causal logic relationships from risks to accidents that are deterministic and non-deterministic relationship. The former can be analyzed by event tree and fault tree. However, due to the latter＇s uncertainties and lack of data, Bayesian network could be applied as an appropriate tool. However, it is a difficult task to determine the conditional probability table. This paper proposes a new allocation method of conditional probability based on fuzzy logic. Combined with the risk model of influencing factors established, Bayesian network is used for quantitative analysis. Finally, the feasibility of proposed method is evaluated through case study.
出处
《计算机系统应用》
2016年第9期27-34,共8页
Computer Systems & Applications
基金
2015陕西省教育厅科学研究基金项目资助项目(15JK2091)
关键词
贝叶斯网络
非确定因果逻辑
模糊逻辑
Bayesian network
non-deterministic causal logic
fuzzy logic
