A comparative approach to the prediction of open-end yarn breaking strength by regression and artificial neural network models

THS TA404.8.A44 2013

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Main Author: AmirHossein Mahlouji
Format: text::Thesis
Language:English
Published: 2023
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spelling my.uniten.dspace-194072023-05-04T14:05:51Z A comparative approach to the prediction of open-end yarn breaking strength by regression and artificial neural network models AmirHossein Mahlouji THS TA404.8.A44 2013 2023-05-03T13:31:46Z 2023-05-03T13:31:46Z 2013-11 Resource Types::text::Thesis https://irepository.uniten.edu.my/handle/123456789/19407 en application/pdf
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description THS TA404.8.A44 2013
format Resource Types::text::Thesis
author AmirHossein Mahlouji
spellingShingle AmirHossein Mahlouji
A comparative approach to the prediction of open-end yarn breaking strength by regression and artificial neural network models
author_facet AmirHossein Mahlouji
author_sort AmirHossein Mahlouji
title A comparative approach to the prediction of open-end yarn breaking strength by regression and artificial neural network models
title_short A comparative approach to the prediction of open-end yarn breaking strength by regression and artificial neural network models
title_full A comparative approach to the prediction of open-end yarn breaking strength by regression and artificial neural network models
title_fullStr A comparative approach to the prediction of open-end yarn breaking strength by regression and artificial neural network models
title_full_unstemmed A comparative approach to the prediction of open-end yarn breaking strength by regression and artificial neural network models
title_sort comparative approach to the prediction of open-end yarn breaking strength by regression and artificial neural network models
publishDate 2023
_version_ 1806424142155612160
score 13.19449