CAJ | 학술논문

针对计算机网络故障诊断知识库冗余性高、神经网络与 PCA、DS 证据等理论相结合诊断精度不高的难题,提出了一种新的基于粗糙集和 BP 神经网络的计算机网络故障诊断模型.首先利用粗糙集算法对网络故障特征进行约简处理、提取最小诊断规则;其次利用最小规则训练 BP神经网络,建立基于粗糙集和 BP 神经网络的计算机网络故障诊断模型;最后将模型运用于真实网络故障数据诊断.结果表明:该模型具有学习效率高、诊断速度快、准确率高的特点,能够快速诊断网络故障类型.
침대계산궤망락고장진단지식고용여성고、신경망락여 PCA、DS 증거등이론상결합진단정도불고적난제,제출료일충신적기우조조집화 BP 신경망락적계산궤망락고장진단모형.수선이용조조집산법대망락고장특정진행약간처리、제취최소진단규칙;기차이용최소규칙훈련 BP신경망락,건립기우조조집화 BP 신경망락적계산궤망락고장진단모형;최후장모형운용우진실망락고장수거진단.결과표명:해모형구유학습효솔고、진단속도쾌、준학솔고적특점,능구쾌속진단망락고장류형.
To deal with the problems of redundancy of network fault diagnosis, the knowledge base and Low Accuracy of neural network model combined with PCAand DS evidence theory are presented in this paper. A new fault diagnosis model of computer network based on rough set and BP neural network is engineered, in which many fault features of computer network are retrieved. These features are then reduced to the minimum diagnosis rules using rough set. The minimum diagnosis rules are trained by BP neural network. The simulation results indicate that the new fault diagnosis model has higher learning efficiency, faster speed of diagnosis and higher detection accuracy.