DocumentCode :
2266950
Title :
Application of Neural Network in Prediction of Brassiere-Wearing Effect
Author :
Chen, Minzhi ; Ying, He ; Zhang, Weiyuan
Author_Institution :
Dept. of Fashion Design & Eng., Dong Hua Univ., Shanghai
Volume :
2
fYear :
2008
fDate :
20-22 Dec. 2008
Firstpage :
1020
Lastpage :
1024
Abstract :
Brassiere as a foundation garment functions not only to protect woman\´s breast, but also to provide formative beauty. In order to meet the individualized need of customers and serve for apparel e-commerce, a prediction model of brassiere-wearing effect based on BP neural networks was established in this research. Firstly, the influencing factors of bust shape change were investigated, including the principal components of naked bust measurements and the structural parameters of brassiere production. Then by using the Matlab neural network toolbox, a back-propagation artificial neural network model was created. It consisted of 6 subnets which could predict the change ratio of a given effect parameter. The function "postreg" was applied to determine the final architecture of each subnet. Finally, it was validated that the predicted result of this model showed satisfying effects.
Keywords :
backpropagation; clothing; mathematics computing; neural nets; principal component analysis; production engineering computing; Matlab neural network toolbox; back-propagation artificial neural network; brassiere-wearing effect; bust shape change; garment functions; Artificial neural networks; Breast; Clothing; Mathematical model; Neural networks; Predictive models; Production; Protection; Shape measurement; Structural engineering; 3D measurement of bust shape; back propagation neural network; brassiere configuration; prediction of brassiere-wearing effect; principal component analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3497-8
Type :
conf
DOI :
10.1109/IITA.2008.192
Filename :
4739917
Link To Document :
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