A supervised learning method in monitoring linear profile
Version 2 2024-06-06, 11:20Version 2 2024-06-06, 11:20
Version 1 2016-10-13, 10:35Version 1 2016-10-13, 10:35
conference contribution
posted on 2024-06-06, 11:20 authored by SZ Hosseinifard, M AbdollahianIn 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.
History
Pagination
233-237Location
Las Vegas, NVPublisher DOI
Start date
2010-04-12End date
2010-04-14ISBN-13
9780769539843Publication classification
EN.1 Other conference paperTitle of proceedings
ITNG2010 - 7th International Conference on Information Technology: New GenerationsPublisher
IEEEUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorksRefWorks
BibTeXBibTeX
Ref. managerRef. manager
EndnoteEndnote
DataCiteDataCite
NLMNLM
DCDC