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	Distribution system fault diagnosis based on improved rough sets with uncertainty
	Dai, Jing ; Sun, Qiuye
	2010-05-10 ; 2010-05-10
会议名称	Advances in Neural Networks - ISNN 2007, Pt 3, Proceedings ; 4th International Symposium on Neural Networks (ISNN 2007) ; Nanjing, PEOPLES R CHINA ; Web of Science ; INSPEC
关键词	FUZZY REDUCTION Computer Science, Theory & Methods
中文摘要	The volume of data with a few uncertainties overwhelms classic information systems in the distribution control center and exacerbates the existing knowledge acquisition process of expert systems. The paper describes a systematic approach for detecting superfluous data. It is considered as a "white box" rather than a "black box" like in the case of neural network. The approach therefore could offer user both the opportunity to learn about the data and to validate the extracted knowledge. To deal with the uncertainty and deferent structures of the system, rough sets and fuzzy sets are introduced. The reduction algorithm based on uncertainty rough sets is improved. The rule reliability is deduced using fuzzy sets and probability. The simulation result of a power distribution system shows the effec-tiveness and usefulness of the approach.
会议录出版者	SPRINGER-VERLAG BERLIN ; BERLIN ; HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
语种	英语 ; 英语
内容类型	会议论文
源URL	[http://hdl.handle.net/123456789/19906]
专题	清华大学
推荐引用方式 GB/T 7714	Dai, Jing,Sun, Qiuye. Distribution system fault diagnosis based on improved rough sets with uncertainty[C]. 见:Advances in Neural Networks - ISNN 2007, Pt 3, Proceedings, 4th International Symposium on Neural Networks (ISNN 2007), Nanjing, PEOPLES R CHINA, Web of Science, INSPEC.

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