本文已被：浏览 1841次 下载 2667次
Received:December 30, 2015 Revised:February 25, 2016
Received:December 30, 2015 Revised:February 25, 2016
中文摘要: 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.
Abstract: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.
文章编号： 中图分类号： 文献标志码：
基金项目:2015陕西省教育厅科学研究基金项目资助项目（15JK2091）
| Author Name | Affiliation |
| LIN Qing | Xi'an Peihua University, Xi'an 710125, China |
| DAI Hui-Jun | Xi'an Jiaotong University, Xi'an 710049, China |
| REN De-Wang | Xi'an Jiaotong University, Xi'an 710049, China |
| Author Name | Affiliation |
| LIN Qing | Xi'an Peihua University, Xi'an 710125, China |
| DAI Hui-Jun | Xi'an Jiaotong University, Xi'an 710049, China |
| REN De-Wang | Xi'an Jiaotong University, Xi'an 710049, China |
引用文本：
林青,戴慧珺,任德旺.基于贝叶斯网络的不确定因果逻辑量化分析方法.计算机系统应用,2016,25(9):27-34
LIN Qing,DAI Hui-Jun,REN De-Wang.Quantitative Analysis Methodology of Non-Deterministic Causal Relationships Based on Bayesian Network.COMPUTER SYSTEMS APPLICATIONS,2016,25(9):27-34
林青,戴慧珺,任德旺.基于贝叶斯网络的不确定因果逻辑量化分析方法.计算机系统应用,2016,25(9):27-34
LIN Qing,DAI Hui-Jun,REN De-Wang.Quantitative Analysis Methodology of Non-Deterministic Causal Relationships Based on Bayesian Network.COMPUTER SYSTEMS APPLICATIONS,2016,25(9):27-34
