An analysis of rule generation in fault diagnosis based on rough set theory

被引：0
|
作者：
Zhao Xi-lin [1 ]
Zhou Jian-zhong [1 ]
Yang Jun-jie [1 ]
Wu Wei [1 ]
Liu Fang [1 ]
机构：
[1] Huazhong Univ Sci & Technol, Coll Hydropower & Informat Engn, Wuhan 430074, Hubei Province, Peoples R China
来源：
Proceedings of 2006 International Conference on Artificial Intelligence: 50 YEARS' ACHIEVEMENTS, FUTURE DIRECTIONS AND SOCIAL IMPACTS | 2006年
关键词：
rough set; fault diagnosis; discernibility; matrix; diagnostic rule; reduction;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Rough set is a new method for the data analysis and disposal. We can reduce attributes to enhance the efficiency by using the Rough set in fault diagnosis. But because the swatch set of the fault is always incomplete, it may delete the attributes that are needed when we use the Rough set to reduce the attributes. The generation of the default diagnostic rules by the use of reduction will result in the inconsistency between diagnosis rules and the fact. This paper proves the probability will occur by the analysis of the example that uses Rough set to generate the diagnostic rules from inconsistent information. The pivotal reason and the method that can resolve the problem are also pointed out. And the resolved algorithm is also put forward.
引用
收藏
页码：694 / 697
页数：4
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