摘要
在两类支持向量机的基础上,综合分级聚类和决策树的思想构造多类支持向量机,从而简化了分类器结构,减少了分类器数量,避免了拒绝分类区的出现,并加快了训练和识别速度。在小样本情况下对多类汽轮发电机组故障进行了诊断研究,结果表明该方法能够正确地对故障进行识别。
The basic support vector machine is designed for two-class problem. A new support vector machine based on hierarchical clustering and decision tree is proposed to solve the multi-class recognition problems. The structure is simplified and the rate of tram and identify is expedited. The results indicate that the algorithm is efficient in the fault diagnosis of turbogenerator unit with small samples.
出处
《华北电力大学学报（自然科学版）》
CAS
北大核心
2003年第6期25-29,共5页
Journal of North China Electric Power University：Natural Science Edition
