A novel approach for recognition of control chart patterns: Type-2 fuzzy clustering optimized support vector machine

- Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation.
- Hence pattern recognition is very useful in identifying process problem.
- In this study, because of the promising generalization capability of support vector machines, a multiclass SVM (SVM) based classifier is proposed.

Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence, pattern recognition is very useful in identifying the process problems. In this study, a multiclass SVM (SVM) based classifier is proposed because of the promising generalization capability of support vector machines. In the proposed method type-2 fuzzy c-means (T2FCM) clustering algorithm is used to make a SVM system more effective. The fuzzy support vector machine classifier suggested in this paper is composed of three main sub-networks: fuzzy classifier sub-network, SVM sub-network and optimization sub-network. In SVM training, the hyper-parameters plays a very important role in its recognition accuracy. Therefore, cuckoo optimization algorithm (COA) is proposed for selecting appropriate parameters of the classifier. Simulation results showed that the proposed system has very high recognition accuracy.