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Control Chart Patterns Recognition based on DAG-SVM
Xiao ZB(肖忠保); Chen SH(陈书宏)
作者部门智能检测与装备研究室
会议名称6th International Conference on Manufacturing Science and Engineering (ICMSE)
会议日期November 28-29, 2015
会议地点Guangzhou, PEOPLES R CHINA
会议录名称PROCEEDINGS OF THE 2015 6TH INTERNATIONAL CONFERENCE ON MANUFACTURING SCIENCE AND ENGINEERING
出版者ATLANTIS PRESS
出版地PARIS
2015
页码1056-1062
收录类别CPCI(ISTP)
WOS记录号WOS:000388457800193
产权排序1
ISSN号2352-5401
ISBN号978-94-6252-137-7
关键词Dag Svm Pso
摘要Statistical process control charts have been widely utilized in manufacturing processes for determining whether a process is run in its intended mode or in the presence of unnatural patterns, it's a multi-class classifier problem. Effective approaches to recognize control chart patterns is essential for a manufacturing process to maintain high-quality products. This paper we use the Directed Acyclic Graph(DAG) tree learning architecture, which combines many two-class classifiers together to solve the multi-class classifier problem. For each node we chose the support vector machine(SVM) using a particle swarm optimization(PSO) algorithm to optimize the parameter of the SVM kernel function. Here the PSO not only takes the kernel function parameters as variables but also the feature vector of the SVM to optimize. Simulation results show the propose algorithm achieves a high recognition accuracy and solve the unable recognition area.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/19490
专题智能检测与装备研究室
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of the Chinese Academy of Sciences, Beijing 100049, China
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Xiao ZB,Chen SH. Control Chart Patterns Recognition based on DAG-SVM[C]. PARIS:ATLANTIS PRESS,2015:1056-1062.
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