Analysis of Alarm Correlation Based on Bayesian Learning
Luoming Meng · Jisuanji gongcheng · 2007
The paper proposes an alarm correlation model based on Bayesian networks among communication networks. It adopts EM algorithm to learn the hidden variables in Bayesian networks. The basic concepts of Bayesian networks are introduced. Thhe paper presents a hierarchical architecture for large communication networks. The fault propagation model is used to model the functional relationship among the sub-networks. The paper also discusses how to construct Bayesian networks from the fault propagation model. According to SDH over DWDM experimental systems, the realization and results of the Bayesian learning are discussed.