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BP Neural Network Model-based Physical Exercises and Dietary Habits Relationships Exploration

With the continuous progress of society, increment of social pressure, people have paid little and little attentions to physical exercises and dietary necessity. Take Beijing, Shanghai, Guangzhou, Shenzhen, Shijiazhuang and Baotou university students as research objects, targeted at physical exercis...

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Detalles Bibliográficos
Autores principales: Guo, Xingwei, Zhang, Xuesheng, Sun, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Bentham Open 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4772760/
https://www.ncbi.nlm.nih.gov/pubmed/26981164
http://dx.doi.org/10.2174/1874120701509010276
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author Guo, Xingwei
Zhang, Xuesheng
Sun, Yi
author_facet Guo, Xingwei
Zhang, Xuesheng
Sun, Yi
author_sort Guo, Xingwei
collection PubMed
description With the continuous progress of society, increment of social pressure, people have paid little and little attentions to physical exercises and dietary necessity. Take Beijing, Shanghai, Guangzhou, Shenzhen, Shijiazhuang and Baotou university students as research objects, targeted at physical exercises time and dietary habits, it starts investigation. Make principal component analysis of investigation results, results indicates that cereal intake is principal component in dietary habits; strenuous exercise time and general physical exercise time are the principal components in physical exercise. Utilize BP neural network model, analyze these seven cities’ physical exercises and dietary habits conditions, the result indicates that except for Shenzhen, all the other six cities haven’t reached the standard.
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spelling pubmed-47727602016-03-15 BP Neural Network Model-based Physical Exercises and Dietary Habits Relationships Exploration Guo, Xingwei Zhang, Xuesheng Sun, Yi Open Biomed Eng J Article With the continuous progress of society, increment of social pressure, people have paid little and little attentions to physical exercises and dietary necessity. Take Beijing, Shanghai, Guangzhou, Shenzhen, Shijiazhuang and Baotou university students as research objects, targeted at physical exercises time and dietary habits, it starts investigation. Make principal component analysis of investigation results, results indicates that cereal intake is principal component in dietary habits; strenuous exercise time and general physical exercise time are the principal components in physical exercise. Utilize BP neural network model, analyze these seven cities’ physical exercises and dietary habits conditions, the result indicates that except for Shenzhen, all the other six cities haven’t reached the standard. Bentham Open 2015-10-09 /pmc/articles/PMC4772760/ /pubmed/26981164 http://dx.doi.org/10.2174/1874120701509010276 Text en © Guo et al.; Licensee Bentham Open. https://creativecommons.org/licenses/by/4.0/legalcode This is an open access articles licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided that the work is properly cited.
spellingShingle Article
Guo, Xingwei
Zhang, Xuesheng
Sun, Yi
BP Neural Network Model-based Physical Exercises and Dietary Habits Relationships Exploration
title BP Neural Network Model-based Physical Exercises and Dietary Habits Relationships Exploration
title_full BP Neural Network Model-based Physical Exercises and Dietary Habits Relationships Exploration
title_fullStr BP Neural Network Model-based Physical Exercises and Dietary Habits Relationships Exploration
title_full_unstemmed BP Neural Network Model-based Physical Exercises and Dietary Habits Relationships Exploration
title_short BP Neural Network Model-based Physical Exercises and Dietary Habits Relationships Exploration
title_sort bp neural network model-based physical exercises and dietary habits relationships exploration
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4772760/
https://www.ncbi.nlm.nih.gov/pubmed/26981164
http://dx.doi.org/10.2174/1874120701509010276
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