摘要
针对自行火炮维修保障中存在大量维修案例没有被记录和整理、维修中故障诊断效率低等现状和诸多不足,提出了将人工智能中的基于案例推理技术(CBR)与人工神经网络技术(ANN)相结合(即CBR-ANN)应用于自行火炮的故障诊断中,建立了故障诊断系统;同时对诊断系统中的几个关键技术——案例的混合聚类模型、案例的表达及基于3层BP神经网络的案例索引进行了设计和说明。该系统的完善和应用有利于提高自行火炮的维修保障能力。
Aimed at many shortages existed in self-propelled gun maintenance, for example, a great number of maintenance cases were not recorded and the effectiveness of fault diagnosis in gun maintenance was low, the artificial intelligent technology （CRB-ANN） based on case-based reasoning technology （CBR） and artificial neural network technology （ANN） was proposed. The CRB-ANN technology can be applied to fault diagnosis of self-propelled gun. Fault diagnosis system was built up. At the same time, several key techniques of fault diagnosis system including the mixed clustering model of the case, case expression and case indexes based on BP neural network of three layers were designed and introduced. The improvement and application of fault diagnosis system are beneficial to improvement of maintenance support capability of self-propelled gun.
出处
《火炮发射与控制学报》
北大核心
2007年第3期62-65,共4页
Journal of Gun Launch & Control
关键词
人工智能
自行火炮
故障诊断
案例推理
神经网络
artificial intelligence
self-propelled gun
fault diagnosis
case-based reasoning
neural network
