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A supervised learning method in monitoring linear profile

conference contribution
posted on 2010-08-11, 00:00 authored by Seyedehzahra Hosseinifard, M Abdollahian
In some practical situations, the quality of a process or product is characterized by a relationship (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 propose a supervised feed forward neural network to detect and classify drift shifts in linear profiles. The proposed method contains three networks and the efficacy of the model is assessed using average run length criterion. © 2010 Crown Copyright.

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Pagination

233 - 237

Publisher

IEEE

Location

Las Vegas, NV

Start date

2010-04-12

End date

2010-04-14

ISBN-13

9780769539843

Publication classification

EN.1 Other conference paper

Title of proceedings

ITNG2010 - 7th International Conference on Information Technology: New Generations

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