【作者】 李千目； 戚湧； 张宏； 刘凤玉；

【Author】 LI Qian-Mu,QI Yong,ZHANG Hong,and LIU Feng-Yu (Department of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing,210094)

【机构】 南京理工大学计算机科学与技术系；

【摘要】 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,综合运用神经网络方法和粗糙集理论,提出了RSNN算法,实现不一致情况下的规则获取和学习样本的净化处理.该算法具有简化样本、适应性强、容错性高和不易陷入局部最小点等特点,能有效处理网络故障诊断中噪声或不相容的信息.实验表明,利用该方法实现的系统与同类的其他方法相比,提高了诊断准确率和诊断速度.更多还原

【Abstract】 In this paper,a design method for network fault diagnosis systems is put forward by proposing RSNN algorithm,which tightly combines neural network and rough sets.Reduced information table can be obtained,which implies that the number of evaluation criteria is reduced with no 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’s 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 a good diagnostic ability.更多还原