摘要
针对一对一（OVO）分解法,提出了一种改进的投票（MWV）策略,解决了传统策略中的不可分区域问题。首先,由训练iω类和ωj（j≠i,j=1,…,n）类而得到的SVM决策函数;再对iω类定义了一个取值在0~1之间的调节函数,并使改进的得票值等于传统得票值加上调节函数。最后,根据改进的得票值进行分类决策。对于可分区域的样本,改进MWV策略的分类结果与传统策略完全相同;对于不可分区域的数据,由调节函数的值决定。将所提法应用于齿轮传动箱故障诊断实例并与传统得票策略诊断进行了对比,实验结果验证了所提方法的上述优越性。
An improved max-wins-voting （MWV） strategy for one-versus-one （OVO） classification is developed and the unclassifiable regions existing in conventional one are resolved. Firstly, using the decision functions obtained by training the SVM for classes wi and ωj（j≠i,j=1,…,n）, for class wi, a novel tuning function is defined in the range of 0-1. Secondly, the improved voting value for class wi equals to the traditional voting value plus the tuning function. Finally, a classification decision is made according to the improved voting value. For the data in the classifiable regions, the classification results using improved MWV strategy are the same as that using the traditional one. Whereas, the data in the unclassifiable region are determined by the tuning func- tion. The comparison is done with experimental data in the application of fault diagnosis for gearbox. Experimental results demonstrate the superiority of the presented strategy.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2009年第4期982-987,共6页
Systems Engineering and Electronics
基金
国家自然科学基金资助课题(50705039)
关键词
模式识别
多类支持向量机
投票法
故障诊断
一对一分解
pattern recognition
multi-class support vector machines
max-wins-voting
fault diagnosis,one-versus-one decomposition
