神经网络在网络故障诊断中的应用研究

Study of Neural Network in Network Fault Diagnosi

研究保护网络的安全性,针对传统网络故障知识库冗余度高和稳定性难以两全的缺陷。为保证对故障实时和精确诊断,可以综合利用神经网络方法和粗糙集理论,提出了RSNN算法,可实现不一致情况下的规则获取和学习样本的净化处理。算法具有简化样本、适应性强和容错性高等优点,克服了传统神经网络易陷入局部最优值的缺陷,能够有效消除网络故障诊断中噪声或不相容的信息。利用上述改进方法进行仿真,结果表明,改进方法与同类的其他方法相比,提高了诊断准确率和诊断速度,为实际应用提供了依据,具有一定的价值。

In this paper,a design method for network fault diagnosis systems is put forward by proposing RSNN algorithm,which tightly combines neural network with rough sets.Reduced information table can be obtained,which implies that the number of evaluation criteria is reduced without information loss through rough set approach.And then,this reduced information is used to develop classification rules and train neural network to infer appropriate parameters.The rules developed by RS-neural network analysis show the best prediction accuracy if a case does match any of the rules.It is capable of overcoming several shortcomings in existing diagnosis systems,such as a dilemma between stability and redundancy.The experiment system implemented by this method shows good diagnostic ability.This new method has practicability.