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《Proceedings of Electric Power System and Automation》 2004-02
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Knowledge Representation and Acquisition Based on Decision Tree for Substation Fault Diagnosis

BAI Jian-she, FAN Bo, HUANG Wen-hua, XUE Jun-yi (College of Electrical Engineering,Xi'an Jiaotong University, Xi'an 710047, China)  
Knowledge representation and acquisition(KRA)is always a bottleneck problem of building artificial intelligence system(AI),which is based on knowledge.This paper aimed at the shortage of the knowledge representation and acquisition methods at present,and proposed a new KRA method based on decision tree(DT).This proposed method used the advantage that the decision tree possesses the knowledge representation and acquisition,and carried out the knowledge representation(KR)and knowledge acquisition(KA)simultaneously,overcome the shortage of the KR and KA separated in traditional AI.Finally,the proposed method was applied to the knowledge representation and acquisition of fault diagnosis for substation,and the result shows that not only it can implement the automatic acquisition and representation of knowledge,but also the acquired knowledge in decision tree possess the greatly high inference efficiency.
【CateGory Index】: TM76;TP277
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