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基于改进HMM模型的组合服务故障诊断方法

DOI: 10.3969/j.issn.1005-3026.2014.05.011, PP. 655-658

Keywords: 隐马尔科夫模型,组合Web服务,故障诊断,贝叶斯估计,特征序列

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Abstract:

针对现有组合Web服务诊断模型故障诊断准确率普遍不高的问题,提出一种新颖的基于改进隐马尔可夫模型(Improved-HMM)的故障诊断方法.首先,从组合服务监测数据中提取多维特征序列训练HMM模型.训练过程中,考虑到基于BW的方法仅在某观测条件下进行参数评估,获得的参数准确度不高,提出基于贝叶斯估计的学习方法,得到更客观的参数;进一步,基于改进的HMM模型计算当前特征序列对应的各类故障类型发生概率,推断最有可能的故障类型.实验结果表明,提出的方法具有较高的诊断率和较低的漏报率,适合在网络环境中进行实时故障检测.

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