[의류제품] A Hybrid Neural Network and Immune Algorithm Approach for Fit Garment Design 출판일 : 2009.09.01 저자 : Zhi-Hua Hu, Yong-Sheng Ding, Xiao-Kun Yu, Wen-Bin Zhang, Qiao Yan 서지사항 : Textile Research Journal, Volume 79, Issue 14, 1319 페이지 등록일 : 2011.05.23 I 조회수 : 115 작성자 : admin |
The purpose of this study was to develop a system to utilize the successful experiences
and help the beginners of garment pattern design (GPD) through optimization methods. Size
design of fit garments is a basic and difficult problem in GPD for inexperienced designers.
We have proposed a hybrid system (NN-ICEA) based on neural network (NN) and immune
co-evolutionary algorithm (ICEA) to predict the fit of the garments and search optimal sizes.
ICEA takes NN as fitness function and procedures including clonal proliferation,
hypermutation and co-evolution search the optimal size values. A series of experiments
with a dataset of 450 pieces of pants was conducted to demonstrate the prediction and
optimization capabilities of NN-ICEA. In the comparative studies, NN-ICEA was compared
with NN-genetic algorithm to show the value of immune-inspired operators. Four types of
GPD method have been summarized and compared. Moreover, the hybrid system for general
features of garment has been discussed. The research is a feasible and effective attempt
aiming at a valuable problem and provides key algorithms for fit prediction and size
optimization. The algorithms can be incorporated into garment computer-aided design
system.
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