Abstract：As a result of the rising demand for services and the resulting increase in size,bandwidth and complexity,fault management in today’s high speed communication networks is becoming even more difficult.When a network problem or failure occurs,it is possible that a very large volume of alarm messages is generated,while alarm correlation is a potentially complex problem.Though some existing alarm correlation systems nowadays have different drawbacks such as lack of scalability,hindered by solving complexity,or no learning process,etc.This paper presents a fault-identification and alarm-correlation method based on improved GA-NN model in communication networks.The experimental results show that this method is simple,which not only overcomes the disadvantages of normal alarm correlation ways,but also improves the dynamic character,training accuracy and efficiency greatly than BP algorithm,BGA algorithm and AGA algorithm do.
王新苗; 晏蒲柳; 黄天锡. 基于改进遗传神经网络模型的通信网络故障识别和告警相关性分析方法[J]. 电子与信息学报, 2000, 22(5): 811-816 .
Wang Xinmiao; Yan Puliu; Huang Tianxi. A FAULT-IDENTIFICATION AND ALARM-CORRELATION METHOD BASED ON IMPROVED GA-NN MODEL IN COMMUNICATION NETWORKS. , 2000, 22(5): 811-816 .
2000, Vol. 22 
