摘要
支持向量机是一种基于结构风险最小原则的新型机器学习方法,具有完备的理论依据和良好的学习泛化能力。该文针对电子装备系统特征,采用支持向量机算法构建智能故障诊断模型,并对典型电子设备进行故障诊断。结果表明,该诊断模型是可行的、有效的,具有一定工程应用价值。
Support vector machines is a new statistical learning method based on structural risk minimization principle,and it has integrated theory and valid learning generalization ability.This paper presents a new electronic equipment sys-tems intelligent fault diagnosis mode based on support vector machines.And then uses it to diagnose for typical equip-ment.The results show that the diagnosis mode is valid and rational.And also it improves the accuracy of fault diagnosis comparing to traditional methods and has great engineering application value.
出处
《计算机工程与应用》
CSCD
北大核心
2003年第22期210-211,232,共3页
Computer Engineering and Applications
基金
国家部委预研基金
部级重点工程资助
关键词
神经网络
机器学习
支持向量机
电子装备
故障诊断
Neural networks,Machine learning,Support vector machines,Electronic equipment ,Fault diagnosis