A framework for expert system development in statistical quality control
Computers and Industrial Engineering
Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems
Computers and Industrial Engineering
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A hybrid system for SPC concurrent pattern recognition
Advanced Engineering Informatics
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
A Research about Pattern Recognition of Control Chart Using Probability Neural Network
CCCM '08 Proceedings of the 2008 ISECS International Colloquium on Computing, Communication, Control, and Management - Volume 02
Control chart pattern recognition using a novel hybrid intelligent method
Applied Soft Computing
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Control chart patterns (CCPs) are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in the manufacturing processes. This paper presents a novel hybrid intelligent method for recognition of common types of CCP. The proposed method includes three main modules: the feature extraction module, the classifier module and optimization module. In the feature extraction module, a proper set of the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module multilayer perceptron neural network and support vector machine (SVM) are investigated. In support vector machine training, the hyper-parameters have very important roles for its recognition accuracy. Therefore, in the optimization module, improved bees algorithm is proposed for selecting of appropriate parameters of the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy.