摘要
针对传统故障知识库冗余度高和稳定性难以两全的缺陷,提出了RSFBP算法,综 合运用神经网络方法和粗糙集理论,实现对学习样本净化处理。实验表明,利用该方法实现 的系统在进行网络故障诊断时可以取得较好的效果。
This paper puts forward a design method for network fault diagnosis systems by proposing RSFBP algorithm, which tightly combines neural network and rough sets. It's capable of overcoming several shortcomings in existing diagnosi s systems, such as a dilemma between stability and redundancy. The experiment sy stem implemented by this method shows a good diagnostic ability.
出处
《计算机工程》
CAS
CSCD
北大核心
2003年第21期6-7,11,共3页
Computer Engineering
基金
国家自然科学基金资助项目(60273137)
国防科工委应用基础基金资助项目( 420010701)
关键词
粗糙集
神经网络
故障诊断
Rough sets
Neural network
Fault diagnosis