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

ISA Trans

Faculty of Electrical Engineering, K.N.Toosi University of Technology, Tehran, Iran; Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran. Electronic address:

Published: July 2016

AI Article Synopsis

  • Control charts often display unnatural patterns that indicate specific causes of process variation, making pattern recognition a critical tool for identifying process issues.
  • This study introduces a multiclass SVM classifier enhanced by a type-2 fuzzy c-means clustering algorithm to improve its effectiveness and generalization capability.
  • The proposed fuzzy support vector machine classifier includes three sub-networks and utilizes the cuckoo optimization algorithm to select optimal hyper-parameters, resulting in significantly high recognition accuracy in simulations.

Article Abstract

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.

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Source
http://dx.doi.org/10.1016/j.isatra.2016.03.004DOI Listing

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