Application of the PSO-SVM model for recognition of control chart patterns.

[1]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[2]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[3]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[4]  Jirí Benes,et al.  On neural networks , 1990, Kybernetika.

[5]  Ercan Oztemel,et al.  Control chart pattern recognition using neural networks , 1992 .

[6]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[7]  Duc Truong Pham,et al.  Control chart pattern recognition using learning vector quantization networks , 1994 .

[8]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[9]  Amjed M. Al-Ghanim,et al.  Automated unnatural pattern recognition on control charts using correlation analysis techniques , 1997 .

[10]  Duc Truong Pham,et al.  Feature-based control chart pattern recognition , 1997 .

[11]  J. C. BurgesChristopher A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .

[12]  J. D. T. Tannock,et al.  On-line control chart pattern detection and discrimination - a neural network approach , 1999, Artif. Intell. Eng..

[13]  R. J. Alcock Time-Series Similarity Queries Employing a Feature-Based Approach , 1999 .

[14]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[15]  Mehmet Erler,et al.  Control Chart Pattern Recognition Using Artificial Neural Networks , 2000 .

[16]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[17]  Ruey‐Shiang Guh Robustness of the neural network based control chart pattern recognition system to non‐normality , 2002 .

[18]  Adnan Hassan,et al.  Improved SPC chart pattern recognition using statistical features , 2003 .

[19]  Yousef Al-Assaf,et al.  Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks , 2004, Comput. Ind. Eng..

[20]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[21]  Qinghong Le,et al.  A new ANN model and its application in pattern recognition of control charts , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[22]  A. Y. Abdelaziz,et al.  A neural network-based scheme for fault diagnosis of power transformers , 2005 .

[23]  Miin-Shen Yang,et al.  A control chart pattern recognition system using a statistical correlation coefficient method , 2005, Comput. Ind. Eng..

[24]  M. Arif Wani,et al.  Parallel algorithm for control chart pattern recognition , 2005, Fourth International Conference on Machine Learning and Applications (ICMLA'05).

[25]  Feng Luan,et al.  QSPR study of permeability coefficients through low-density polyethylene based on radial basis function neural networks and the heuristic method , 2006 .

[26]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[27]  Zheng Chen,et al.  A hybrid system for SPC concurrent pattern recognition , 2007, Adv. Eng. Informatics.

[28]  Zhiqiang Cheng,et al.  A Research about Pattern Recognition of Control Chart Using Probability Neural Network , 2008, 2008 ISECS International Colloquium on Computing, Communication, Control, and Management.

[29]  Shankar Chakraborty,et al.  Recognition of control chart patterns using improved selection of features , 2009, Comput. Ind. Eng..