基于神经网络的告警关联

告警关联技术的研究是网络故障管理的中心问题.介绍网络告警关联的基本理论,分析几种用于告警关联的人工智能方法的局限性,提出一种基于神经网络的告警关联方案.该方案中网络权值调整采用反向传播的学习算法,并在Matlab仿真环境下以实际网络的连接中断故障为例,对该关联算法进行了仿真实验.结果表明,基于BP网络的关联算法能够在有限噪声范围内达到正确的告警关联.该方法在处理不确定性信息和抑制噪声方面较码书算法有所改善,具有一定的实用价值.

A Neural Network Apporach to Alarm Correlation

At the heart of network fault management is the alarm correlation. The paper introduces the basic theory of network alarm correlation. It then analyzes the limitations of some other artificial intelligent correlation methods. An approach based on neural network techniques is presented to solve alarm correlation. Back propagation arithmetic is used to regulate the weight of neural network. As the example of a link fails in the real network, the correlation approach is simulated in the environment of Matlab. The results show that this approach can correlate correctly with noise within a limited range. It has advantages in dealing with uncertain information and in restraining noises, and has a certain practical value.