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Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network
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| Zeitschriftentitel: | Journal of Engineered Fibers and Fabrics |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | Journal of Engineered Fibers and Fabrics, 15, 2020, S. 155892501990083 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
SAGE Publications
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| Schlagwörter: |
| author_facet |
Li, Xintong Cong, Honglian Gao, Zhe Dong, Zhijia Li, Xintong Cong, Honglian Gao, Zhe Dong, Zhijia |
|---|---|
| author |
Li, Xintong Cong, Honglian Gao, Zhe Dong, Zhijia |
| spellingShingle |
Li, Xintong Cong, Honglian Gao, Zhe Dong, Zhijia Journal of Engineered Fibers and Fabrics Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network General Materials Science |
| author_sort |
li, xintong |
| spelling |
Li, Xintong Cong, Honglian Gao, Zhe Dong, Zhijia 1558-9250 1558-9250 SAGE Publications General Materials Science http://dx.doi.org/10.1177/1558925019900837 <jats:p> In this article, thermal resistance test and water vapor resistance test were experimented to obtain data of heat and humidity performance. Canonical correlation analysis was used on determining influence of basic fabric parameters on heat and humidity performance. Thermal resistance model and water vapor resistance model were established with a three-layered feedforward-type neural network. For the generalization of the network and the difficulty of determining the optimal network structure, trainbr was chosen as training algorithm to find the relationship between input factors and output data. After training and verification, the number of hidden layer neurons in the thermal resistance model was 12, and the error reached 10<jats:sup>−3</jats:sup>. In the water vapor resistance model, the number of hidden layer neurons was 10, and the error reached 10<jats:sup>−3</jats:sup>. </jats:p> Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network Journal of Engineered Fibers and Fabrics |
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Journal of Engineered Fibers and Fabrics |
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| title |
Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| title_unstemmed |
Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| title_full |
Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| title_fullStr |
Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| title_full_unstemmed |
Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| title_short |
Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| title_sort |
thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| topic |
General Materials Science |
| url |
http://dx.doi.org/10.1177/1558925019900837 |
| publishDate |
2020 |
| physical |
155892501990083 |
| description |
<jats:p> In this article, thermal resistance test and water vapor resistance test were experimented to obtain data of heat and humidity performance. Canonical correlation analysis was used on determining influence of basic fabric parameters on heat and humidity performance. Thermal resistance model and water vapor resistance model were established with a three-layered feedforward-type neural network. For the generalization of the network and the difficulty of determining the optimal network structure, trainbr was chosen as training algorithm to find the relationship between input factors and output data. After training and verification, the number of hidden layer neurons in the thermal resistance model was 12, and the error reached 10<jats:sup>−3</jats:sup>. In the water vapor resistance model, the number of hidden layer neurons was 10, and the error reached 10<jats:sup>−3</jats:sup>. </jats:p> |
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| author | Li, Xintong, Cong, Honglian, Gao, Zhe, Dong, Zhijia |
| author_facet | Li, Xintong, Cong, Honglian, Gao, Zhe, Dong, Zhijia, Li, Xintong, Cong, Honglian, Gao, Zhe, Dong, Zhijia |
| author_sort | li, xintong |
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| container_title | Journal of Engineered Fibers and Fabrics |
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| description | <jats:p> In this article, thermal resistance test and water vapor resistance test were experimented to obtain data of heat and humidity performance. Canonical correlation analysis was used on determining influence of basic fabric parameters on heat and humidity performance. Thermal resistance model and water vapor resistance model were established with a three-layered feedforward-type neural network. For the generalization of the network and the difficulty of determining the optimal network structure, trainbr was chosen as training algorithm to find the relationship between input factors and output data. After training and verification, the number of hidden layer neurons in the thermal resistance model was 12, and the error reached 10<jats:sup>−3</jats:sup>. In the water vapor resistance model, the number of hidden layer neurons was 10, and the error reached 10<jats:sup>−3</jats:sup>. </jats:p> |
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| physical | 155892501990083 |
| publishDate | 2020 |
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| publisher | SAGE Publications |
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| series | Journal of Engineered Fibers and Fabrics |
| source_id | 49 |
| spelling | Li, Xintong Cong, Honglian Gao, Zhe Dong, Zhijia 1558-9250 1558-9250 SAGE Publications General Materials Science http://dx.doi.org/10.1177/1558925019900837 <jats:p> In this article, thermal resistance test and water vapor resistance test were experimented to obtain data of heat and humidity performance. Canonical correlation analysis was used on determining influence of basic fabric parameters on heat and humidity performance. Thermal resistance model and water vapor resistance model were established with a three-layered feedforward-type neural network. For the generalization of the network and the difficulty of determining the optimal network structure, trainbr was chosen as training algorithm to find the relationship between input factors and output data. After training and verification, the number of hidden layer neurons in the thermal resistance model was 12, and the error reached 10<jats:sup>−3</jats:sup>. In the water vapor resistance model, the number of hidden layer neurons was 10, and the error reached 10<jats:sup>−3</jats:sup>. </jats:p> Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network Journal of Engineered Fibers and Fabrics |
| spellingShingle | Li, Xintong, Cong, Honglian, Gao, Zhe, Dong, Zhijia, Journal of Engineered Fibers and Fabrics, Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network, General Materials Science |
| title | Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| title_full | Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| title_fullStr | Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| title_full_unstemmed | Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| title_short | Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| title_sort | thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| title_unstemmed | Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network |
| topic | General Materials Science |
| url | http://dx.doi.org/10.1177/1558925019900837 |