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Application of artificial neural networks in linear profile monitoring
Authors:S.Z. Hosseinifard  M. Abdollahian  P. Zeephongsekul
Affiliation:1. Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran;2. Université de Nantes & LS2N UMR CNRS 6004, Nantes, France;1. Institute of Statistics, Nankai University, Tianjin, China;2. Beijing Institute of Control Engineering, Beijing, China;1. Bargh Gostar Baharan Golestan Corporation, P.O. Box 4971684981, Gonbad Kavus, Iran;2. Faculty of Electrical and Computer Engineering, Babol (Noushirvani) University of Technology, P.O. Box 47135-484, Babol, Iran;1. Jilin Medical University, Jilin 132013, PR China;2. Institute of Statistics and LPMC, Nankai University, Tianjin 300071, PR China;3. School of Science, Tianjin University of Technology and Education, Tianjin 300222, PR China;1. Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran;2. Faculty of Electrical Engineering, K. N. Toosi University of Technology, Iran;3. Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Cyprus
Abstract:In many quality control applications the quality of process or product is characterized and summarized by a relation (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we use artificial neural networks to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial neural networks are developed to monitor linear profiles. Their efficacies are assessed using average run length criterion.
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