A Fault Diagnosis Method Combining Rough Sets And Neural Network

摘要：

Rough sets and neural networks are two common techniques applied to data mining problems in order to inprove diagnosis precision and decreasing misinformation diagnosis.lntegrating the advantages of two approaches, this paper presents a hybrid system to extract efficiently classification rules from decision table. The target is mainly to remove redundant information and seek for reduced decision tables which to obtain he minimum fault feature subset. The neural networks adopted were of thefeed-forward variety with one hidden layer. They weretrained using back propagation.The effectiveness of our approach was verified by the experiments comparing with traditional rough set and neural network approaches, and can detect the composed faults while keep good robustness.

关键词： rough sets neural networkn data mining classification

作者: Yang Jie

作者单位: Zhejiang Financial College Hangzhou,310018,China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 1435-1438

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）