基于模糊概率的多状态贝叶斯网络可靠性分析  

Reliability analysis of multi-state Bayesian networks based on fuzzy probability

作　　者：马德仲[1,2] 周真[1,2] 于晓洋[1,2] 樊尚春[3] 邢维巍[3] 郭占社[3] 

机构地区：[1]哈尔滨理工大学测控技术与通信工程学院,黑龙江哈尔滨150040 [2]测控技术与仪器黑龙江省高校重点实验室,黑龙江哈尔滨150040 [3]北京航空航天大学仪器科学与光电工程学院,北京100191

出　　处：《系统工程与电子技术》2012年第12期2607-2611,共5页Systems Engineering and Electronics

基　　金：国家高技术研究发展计划(863计划)重点项目(2008AA042207);国家高技术研究发展计划(863计划)专题项目(2009AA04Z407);黑龙江省研究生创新科研资金项目(YJSCX2011-048HLJ)资助课题

摘　　要：利用贝叶斯网络对多状态系统进行可靠性分析时,各根节点不同状态的精确概率难以获得。因此提出了把模糊理论与贝叶斯网络方法相结合,将不同专家给出的根节点各状态发生概率的语言变量转化为三角模糊数,并经过均值化、解模糊和归一化得到不同状态的发生概率的精确值。将其代入多状态贝叶斯网络中,计算叶节点不同状态的发生概率,进而计算各根节点的后验概率及风险增加当量重要度。通过实例分析验证了该方法的可行性。应用该方法能够提高贝叶斯网络处理不确定性问题的能力,使其在解决多状态不确定性系统可靠性和安全性问题时发挥更大的作用。In the course of reliability analysis on multi-state systems by using Bayesian networks, it is difficult to obtain the precision probability of different states of root nodes. So a method combining fuzzy theory with Bayesian networks is proposed. The linguistic variables from different experts, which denote the probability of different states of root nodes, are translated into triangular fuzzy-numbers. By steps viz. equalization, defuzzification and normalization, the precision probabilities are got. Inputting them into multi-state Bayesian networks, the probabilities of leaf nodes in different states are calculated. And then, the posterior probability and risk achievement worth （RAW） importance of root node are got. The feasibility of this method is validated by an example. The application of this method can improve the ability of Bayesian networks to deal with uncertainty issues and make it play a greater role in improving the reliability and security of the multi-state uncertainty system.

关 键 词：可靠性 贝叶斯网络 多状态系统 三角模糊数 模糊概率 

分 类 号：TB114.3[理学—概率论与数理统计]