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Multivariate Mean Shift Diagnostic Model Based on Support Vector Machine
Cai YJ(蔡亚军)1,2; Chen SH(陈书宏)1; Wang Y(王宇)1
作者部门智能检测与装备研究室
会议名称7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
会议日期July 31 - August 4, 2017
会议地点Hawaii, USA
会议主办者IEEE Robotics and Automation Society
会议录名称2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
出版者IEEE
出版地New York
2017
页码713-718
收录类别EI ; CPCI(ISTP)
EI收录号20183905873437
WOS记录号WOS:000447628700129
产权排序1
ISBN号978-1-5386-0489-2
关键词Quality Monitoring Quality Characteristics Principal Component Analysis(Pca) Support Vector Machine(Svm) Modified Grid Algorithm
摘要

Quality monitoring can effectively improve product quality and production efficiency. In the production process of complex products, the interaction of multiple quality characteristics affects the quality of production jointly. The large number of quality characteristics and coupled relationship of some characteristics enhance the difficulty of accurate diagnosis of abnormal variables. In order to diagnose the abnormal variables accurately and improve product quality and production efficiency, this paper proposes a model of monitoring the mean shift based on the improved grid optimization principal component analysis(PCA)-support vector machines(SVM). Before training the model, principal component analysis(PCA) algorithm is used to process the data to reduce data dimension and extract data feature information. Then, this paper uses the modified grid algorithm to optimize the parameters of support vector machine (SVM). Finally, the optimized SVM model is attained. The simulation results show that the proposed method has better performance than the traditional methods.

语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/22824
专题智能检测与装备研究室
通讯作者Cai YJ(蔡亚军)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
推荐引用方式
GB/T 7714
Cai YJ,Chen SH,Wang Y. Multivariate Mean Shift Diagnostic Model Based on Support Vector Machine[C]//IEEE Robotics and Automation Society. New York:IEEE,2017:713-718.
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