[1]郭子翊,黄振华,龙 颖,等.多因素耦合翻立领样板生成模型构建[J].服装学报,2022,7(06):486-492.
　GUO Ziyi,HUANG Zhenhua,LONG Ying,et al.Study on Multi-Factor Coupling Model of the Auto-Generation of Collar Flat Sketch[J].Journal of Clothing Research,2022,7(06):486-492.
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多因素耦合翻立领样板生成模型构建()
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《服装学报》[ISSN:2096-1928/CN:32-1864/TS]

卷:
第7卷
期数:
2022年06期
页码:
486-492
栏目:
服装信息与工程
出版日期:
2022-12-30

文章信息/Info

Title:
Study on Multi-Factor Coupling Model of the Auto-Generation of Collar Flat Sketch
作者:
郭子翊1;  黄振华1;  龙 颖1;  邹奉元*1; 2; 3
1.浙江理工大学 服装学院,浙江 杭州 310018; 2.浙江理工大学 丝绸文化传承与产品设计数字化技术文化和旅游部重点实验室,浙江 杭州 310018; 3.浙江理工大学浙江省服装工程技术研究中心,浙江 杭州 310018
Author(s):
GUO Ziyi1;  HUANG Zhenhua1;  LONG Ying1;  ZOU Fengyuan*1;  2;  3
1.School of Fashion Design and Engineering,Zhejiang Sci-Tech University, Hangzhou 310018, China; 2. National Virtual Simulation Experimental Teaching Center of Clothing Design,Zhejiang Sci-Tech University, Hangzhou 310018, China; 3. Clothing Engineering Research Cencter of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou 310018, China
分类号:
TS 941.63
文献标志码:
A
摘要:
为提高样板生成预测精度,以翻立领为例,提出了一种基于Lasso和PSO-RBF神经网络的面料与款式图参数耦合的样板生成模型。将翻立领款式图样板数据与面料参数输入Lasso模型中进行降维,根据降维结果,建立针对不同领型样板数据采用不同输入方式的PSO-RBF神经网络模型,通过PSO算法得到RBF神经网络的最优权值和模型宽度,最终得到翻立领的样板参数。实验结果表明,该方法与未融入面料因素的PSO-RBF神经网络模型相比,间隙量和起翘量的均方误差分别降低了0.46 cm和0.21 cm。
Abstract:
To solve the problem of not considering the fabric factor in the automatic pattern generation, a multi-factor coupled pattern generation method based on Lasso and PSO-RBF neural network was proposed, taking the lapel collar as an example. Firstly, the style parameters and fabric parameters obtained from the lapel pattern data were input into the Lasso model for dimensionality reduction, then, different input methods were used for different collar pattern data to input them into the PSO-RBF neural network model, according to the dimensionality reduction result. The parameters of the sample were obtained. The comparison experiments results showed that the gap and MSE of the PSO-RBF neural network model was 0.46 cm and 0.21 cm lower than that of the PSO-RBF neural network model without the fabric factor and the commonly used BP model respectively.

参考文献/References:

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(责任编辑:卢 杰)

更新日期/Last Update: 2022-12-30