The Application of Tolerant Rough Set Neural Network to Fighter Fault Diagnosis

摘要：

　　Conventional rough set theory is based on indiscernibility relation,which lacks the adaptive ability to data noise or data missing.Furthermore,it may present qualitatively whether or not the faults exist,but it cant compute accurately the value of the faults.Though the neural network has ability of approximating unknown nonlinear systems,but it cant distinguish the redundant knowledge from useful knowledge,so its classification ability cant catch up with the rough set classifier.This paper combines the rough set theory and the tolerant rough set neural network to diagnose the rudder faults of fighter,which solves well the problem of fault diagnosis and fault degree computation.Simulation results demonstrate the effectiveness of the proposed method.

关键词： Rough Set Tolerant Relation Fault Diagnosis Neural Network

作者: Guoqiang Sun Hongli Wang Jun Tao Xubing Li

作者单位: The Second Artillery Engineering University Xian,China;Aviation University of Air Force Changchun,C The Second Artillery Engineering University Xian,China Aviation University of Air Force Changchun,China

会议类型: 国际会议

会议名称: 2013 International Conference on Energy Research and Power Engineering(2013能源研究与电力工程国际会议)(ERPE2013)

会议地点: 郑州

会议语种:英文

页码: 658-663

在线出版日期: 2013-05-24（万方平台首次上网日期，不代表论文的发表时间）