基于决策树的变电站故障诊断知识表示与获取

针对传统的知识表示与获取方法的不足,提出基于决策树的知识表示与获取方法.该方法充分利用决策树把知识表示与获取融于一身的优点,使知识表示与知识获取同时进行,克服了传统人工智能系统中知识表示与知识获取分离的缺点.将其用于变电站故障诊断知识的获取与表示中,结果表明,提出的方法不仅能够实现知识的自动获取与表示,而且所获得的以决策树形式表示的知识具有很高的推理效率.

Knowledge Representation and Acquisition Based on Decision Tree for Substation Fault Diagnosis

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.