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 。
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
出处
《计算机研究与发展》
EI
CSCD
北大核心
2004年第10期1696-1702,共7页
Journal of Computer Research and Development
基金
国家自然科学基金项目 ( 60 2 73 13 7)
关键词
粗糙集
神经网络
故障诊断
rough sets
neural network
fault diagnosis
