摘要
基于支持向量数据描述良好的分类性能,针对旋转机械故障诊断中故障样本获取的特点,提出了基于正负类样本的加权模糊支持向量数据描述多类分类器,不仅考虑了正类样本,而且也充分考虑了负类样本对分类结果的影响。利用模拟故障样本对系统进行了实验,结果表明提出的方法在系统中具有良好的分类能力。
Based on the favorable classification performance of support vector data description（SVDD）, aiming at the problem of fault samples＇ acquisition in rotating machinery fault diagnosis, this paper proposed a fuzzy support vector data description classifier based on positive and negative samples,which can be used to deal with the outlier sensitivity problem in traditional multi-class classification problems. This method considers the effect of negative samples to classification results, as well as the positive samples in the traditional SVDD algorithm. Experimental results show that the proposed method can reduce the effect of outliers and yield higher classification rate than other existing methods.
出处
《计算机科学》
CSCD
北大核心
2009年第7期182-184,229,共4页
Computer Science
基金
国家自然科学基金重大研究计划(No.90718030)
辽宁省科技厅博士启动基金(No.20081079)
辽宁省教育厅科学技术研究项目(No.2008347)资助
关键词
支持向量数据描述
加权
分类器
支持向量机
Support vector data description,Weighting,Classifier, Support vector machine
