@article{(Open Science Index):https://publications.waset.org/pdf/416,
	  title     = {Control Chart Pattern Recognition Using Wavelet Based Neural Networks},
	  author    = {Jun Seok Kim and  Cheong-Sool Park and  Jun-Geol Baek and  Sung-Shick Kim},
	  country	= {},
	  institution	= {},
	  abstract     = {Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {6},
	  number    = {12},
	  year      = {2012},
	  pages     = {1717 - 1721},
	  ee        = {https://publications.waset.org/pdf/416},
	  url   	= {https://publications.waset.org/vol/72},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 72, 2012},
	}