摘要
支持向量机是一种基于统计学习理论的新型机器学习方法,它具有在训练样本很少的情况下达到很好的分类效果的优点。把支持向量机技术应用于齿轮故障诊断,通过预先使用局部、全局核函数支持向量机的分类结果适当选取各自在混合函数中的权重,来作为混合核函数进行支持向量机分类。实验和数据分析证明,使用混合核的支持向量机比单独使用全局或局部核函数的分类效果要好。
Support Vector Machines（SVM） is a new machine learning method based on statistics learning theory which has good classification ability even if the training samples are very few.The technology of SVM is applied to gear fault diagnosis.Using of local,global kernel function for samples training classification,then selects their respective weight appropriately by using the previous classification results to construct mixed kernel function.Experiments and data analysis prove that the classification results using mixed kernel function are better than that using local or global kernel function alone.
出处
《机械传动》
CSCD
北大核心
2011年第9期45-47,57,共4页
Journal of Mechanical Transmission
关键词
支持向量机
混合核函数
齿轮故障分类
Support vector machine Mixed kernel function Gear fault classification
