Application of artificial neural networks in linear profile monitoring

Authors:
S. Z. Hosseinifard;M. Abdollahian;P. Zeephongsekul
Affiliations:
Department of Statistics and Operations Research, RMIT University, Melbourne, Australia;Department of Statistics and Operations Research, RMIT University, Melbourne, Australia;Department of Statistics and Operations Research, RMIT University, Melbourne, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 4
Cited 1

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Monitoring general linear profiles using simultaneous confidence sets schemes

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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.