Neural Networks
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems
Computers and Industrial Engineering
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Automated unnatural pattern recognition on control charts using correlation analysis techniques
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
Parallel Algorithm for Control Chart Pattern Recognition
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
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
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A control chart pattern recognition system using a statistical correlation coefficient method
Computers and Industrial Engineering
Computers and Industrial Engineering
Recognition of control chart patterns using an intelligent technique
Applied Soft Computing
Journal of Intelligent Manufacturing
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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 (HIM) for recognition of common types of CCP. The proposed method includes three main modules: a feature extraction module, a classifier module and an optimization module. In the feature extraction module, the multi-resolution wavelets (MRW) are proposed as the effective features for representation of CCPs. These features are novel in this area. In the classifier module, because of the promising generalization capability of support vector machines, a multi-class SVM (SVM) based classifier is proposed. In support vector machine training, the hyper-parameters have very important roles for its recognition accuracy. Therefore, in the optimization module, an efficient genetic algorithm is proposed for selecting of appropriate parameters of the classifier. Simulation results confirm that the proposed system outperforms other methods and shows high recognition accuracy about 99.37%.