计算机科学 ›› 2015, Vol. 42 ›› Issue (7): 62-67.doi: 10.11896/j.issn.1002-137X.2015.07.014
陈 倩,佘 维,叶阳东
CHEN Qian, SHE Wei and YE Yang-dong
摘要: 针对大规模复杂系统建模分析时的状态爆炸问题,提出一种扩展了时间因素的分层贝叶斯Petri网模型(Hierarchical Bayesian Petri Net With Time Factor,TF-HBPN),并基于该模型提出一种递归构建方法和递归溯因行为分析方法。该方法首先对观测到的系统行为构造顶层TF-HBPN模型,进而通过分层递归方法将复杂系统并发行为分析问题进行分解,并通过递归溯因推理和时序分析来计算非观测系统行为及其事件链的发生概率,最后将分析结果与正常动作事件链进行对比,分离出干扰信息。实例分析表明,该方法可对大规模复杂系统行为进行快速建模分析,当观测数据存在干扰和缺失时仍能进行系统行为的分析和现象的溯因,分析结果的可信度较高。与其他基于Petri网的复杂系统分析方法相比,该方法建模难度更低,模型表达更为简洁也更易理解。
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