摘要
基于结构风险最小化原则的支持向量机 ( SVM)对小样本决策具有较好的学习推广性。但由于常规 SVM算法是从 2类分类问题推导出的 ,在解决故障诊断这种典型的多类分类问题时存在困难 ,因而提出一种依赖故障优先级的基于 SVM的二叉树多级分类器实现 ( 2 PTMC)方法 ,该方法具有简单、直观 ,重复训练样本少的优点。通过将其应用于柴油机振动信号的故障诊断 。
Support vector machines is a new general machine learning tool based on structural risk minimization principle that exhibits good generalization. Fault diagnosis based on support vector machines is discussed. Since SVM was originally designed for binary classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification named 2PTMC is presented. This classifier is a binary tree classifier composed of several SVMs organized fault priority, which is simple and has little duplicating training samples. The application to fault diagnosis for diesel engine shows the effectiveness of the method.
出处
《控制与决策》
EI
CSCD
北大核心
2003年第3期272-276,284,共6页
Control and Decision
基金
教育部高校博士点基金资助项目 ( 990 61116)
关键词
支持向量机
故障诊断
二叉树
Algorithms
Classification (of information)
Learning systems
Vectors
