Control Chart Patterns Recognition based on DAG-SVM
- 10.2991/icmse-15.2015.192How to use a DOI?
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.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhongbao Xiao AU - Shuhong Chen PY - 2015/12 DA - 2015/12 TI - Control Chart Patterns Recognition based on DAG-SVM BT - Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering PB - Atlantis Press SP - 1056 EP - 1062 SN - 2352-5401 UR - https://doi.org/10.2991/icmse-15.2015.192 DO - 10.2991/icmse-15.2015.192 ID - Xiao2015/12 ER -