中国机械工程2017，Vol.28Issue(8)：930-935,6.DOI:10.3969/j.issn.1004-132X.2017.08.009
基于融合特征约减和支持向量机的控制图模式识别
Control Chart Pattern Recognition Based on Fusion Feature Reduction and SVM
摘要
Abstract
In order to improve the intelligence of quality monitoring in machining processes,the paper proposed a control chart classification method based on fusion feature reduction and KPCA-SVM,on the basis of quality fluctuation which was described by control chart.Firstly,the Monte Carlo method was applied to generate the control chart data sets,statistical features and shape fea-tures were extracted to fuse with original features,then kernel principal component analysis was ap-plied to reduce dimensionality of high dimensional fusion feature sets.Finally,genetic algorithm was used to optimize parameters of SVM.Recognition accuracy were compared through the simulation ex-periments with the applications of dimensionality reduction and different classification models,the re-sults demonstrate that the higher recognition accuracy may be achieved by using the proposed method.关键词
控制图/模式识别/特征融合/降维/核主成分分析/支持向量机Key words
control chart/pattern recognition/feature fusion/dimension reduction/kernel prin-cipal component analysis(KPCA)/support vector machine(SVM)分类
机械制造引用本文复制引用
赵春华,汪成康,华露,郑思宇,梁志鹏..基于融合特征约减和支持向量机的控制图模式识别[J].中国机械工程,2017,28(8):930-935,6.基金项目
国家自然科学基金资助项目(51205230) （51205230）
湖北省自然科学基金资助项目(2015CFB445) （2015CFB445）
宜昌市自然基础科学研究与应用项目(A15-302-a02) （A15-302-a02）
赛尔网络下一代互联网技术创新项目(NGⅡ20150801). （NGⅡ20150801）
