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Application of BP Neural Networks in Garment Pattern Design System

With the intensification of global market competition and the continuous development of the information technology, competition in the apparel market has become increasingly fierce. The key to whether China's garment industry can maintain its advantage in the international market competition in...

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Autores principales: Ma, Shuo, Yan, Xiaoyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113887/
https://www.ncbi.nlm.nih.gov/pubmed/35592711
http://dx.doi.org/10.1155/2022/8766137
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author Ma, Shuo
Yan, Xiaoyu
author_facet Ma, Shuo
Yan, Xiaoyu
author_sort Ma, Shuo
collection PubMed
description With the intensification of global market competition and the continuous development of the information technology, competition in the apparel market has become increasingly fierce. The key to whether China's garment industry can maintain its advantage in the international market competition in the future lies in whether it can promote and realize the informatization of the garment industry or not. After all, under the context of increasingly developed information technologies and growing competition in the garment market, mass customization of garments has become a future trend in the garment industry. As custom-made clothing is more in line with consumers' individual needs in terms of style, fabric, and size, the focus of development for clothing companies is increasingly on the grasp of the fit of clothing. However, with China's large population and the wide variety of body types, traditional hand-made garments are time-consuming and cannot meet the differentiated needs of consumers in the modern market. The design of garment samples is an important part of the industrial production of garments and is highly dependent on the skills and experience of the operators. In other words, the level of technical expertise can determine the quality and shape of a garment product to a certain extent. As a result, in order to further improve the efficiency and quality of garment sample design and to reduce the dependence on operator skills and experience, this study proposes an intelligent garment paper sample design system based on BP neural networks. The system mainly utilizes the self-learning, self-organizing, and adaptive as well as nonlinear mapping functions of artificial neural networks to design clothing samples autonomously, thus improving the design efficiency. In the era of rapid development of information technology and artificial intelligence technology, the development of intelligent garment pattern design systems with independent intellectual property rights is of great significance in promoting the prosperity of the garment industry.
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spelling pubmed-91138872022-05-18 Application of BP Neural Networks in Garment Pattern Design System Ma, Shuo Yan, Xiaoyu Comput Intell Neurosci Research Article With the intensification of global market competition and the continuous development of the information technology, competition in the apparel market has become increasingly fierce. The key to whether China's garment industry can maintain its advantage in the international market competition in the future lies in whether it can promote and realize the informatization of the garment industry or not. After all, under the context of increasingly developed information technologies and growing competition in the garment market, mass customization of garments has become a future trend in the garment industry. As custom-made clothing is more in line with consumers' individual needs in terms of style, fabric, and size, the focus of development for clothing companies is increasingly on the grasp of the fit of clothing. However, with China's large population and the wide variety of body types, traditional hand-made garments are time-consuming and cannot meet the differentiated needs of consumers in the modern market. The design of garment samples is an important part of the industrial production of garments and is highly dependent on the skills and experience of the operators. In other words, the level of technical expertise can determine the quality and shape of a garment product to a certain extent. As a result, in order to further improve the efficiency and quality of garment sample design and to reduce the dependence on operator skills and experience, this study proposes an intelligent garment paper sample design system based on BP neural networks. The system mainly utilizes the self-learning, self-organizing, and adaptive as well as nonlinear mapping functions of artificial neural networks to design clothing samples autonomously, thus improving the design efficiency. In the era of rapid development of information technology and artificial intelligence technology, the development of intelligent garment pattern design systems with independent intellectual property rights is of great significance in promoting the prosperity of the garment industry. Hindawi 2022-05-10 /pmc/articles/PMC9113887/ /pubmed/35592711 http://dx.doi.org/10.1155/2022/8766137 Text en Copyright © 2022 Shuo Ma and Xiaoyu Yan. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ma, Shuo
Yan, Xiaoyu
Application of BP Neural Networks in Garment Pattern Design System
title Application of BP Neural Networks in Garment Pattern Design System
title_full Application of BP Neural Networks in Garment Pattern Design System
title_fullStr Application of BP Neural Networks in Garment Pattern Design System
title_full_unstemmed Application of BP Neural Networks in Garment Pattern Design System
title_short Application of BP Neural Networks in Garment Pattern Design System
title_sort application of bp neural networks in garment pattern design system
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113887/
https://www.ncbi.nlm.nih.gov/pubmed/35592711
http://dx.doi.org/10.1155/2022/8766137
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