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《Journal of Northeastern University(Natural Science)》 2004-04
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A Knowledge Acquisition by Rough Set Based on Incomplete Data Sets and Its Application in Fault Diagnosis for Electricity Networks

ZHANG Tie-yan, SUN Qiu-ye, ZHANG Hua-guang, LIU Li (School of Information Science & Engineering, Northeastern University, Shenyang 110004, China. )  
A newly reduced algorithm based on rough set(RS) theory is proposed for fault diagnosis in case no complete data are available when the power network failed and then caused possibly the protections/breakers to malfunction and/or the transmission defects in communication. Furthermore, the reduced result can be synthesize into one as a tabulated expert decision library. With fuzzy sets and probability applied to the rules of rough sets, the confidence levels of each rule and relevant equipment are taken into consideration and computed. Another algorithm is also proposed to analyze synthetically the confidence level of a diagnostic conclusion in accordance to the number of rules for a certain decision-making process and the confidence level of each rule, which is also to be put into application to power network fault diagnosis. In the example given as fault diagnosis, the reduction of the tabulated decisions is implemented through the VB-based programming language, of which the results show the effectiveness and practicability of the method proposed here.
【Fund】: 国家自然科学基金资助项目(60274017);; 沈阳市科技攻关项目(1023090 2 00)·
【CateGory Index】: TM711
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