BP神经网络下的智能化合体服装样板生成

为快速得出符合顾客体型的服装样板，以男西裤为基准样板，腰围和臀围部位的变更规则为研究对像，格柏CAD 为技术开发平台，依据大量人体数据对样板原有的变更规则进行优化和重建，采用BP 神经网络算法建立了人体腰围、臀围尺寸变化量与相应的变更规则之间的神经网络模型。直接采用数据与数据的匹配归档，最终实现男西裤腰腹部和臀部变更规则的参数化设计，即给定一个腰围、臀围的变化量就得到一个相应的变更规则，通过调用此变更规则就会自动得出符合这个尺寸的样片，初步实现一人一板，提高了服装的合体度，减少了对样板师的依赖性。

Generation of intelligent fitting pattern based on BP neural network

In order to quickly obtain a clothing model fitting the customer's body shape, the men's trousers were used as the reference model, and the rules for changing the waist and hip areas were used as research subjects. Gerber CAD was used as a technology development platform. Based on a largequantity of human body data, the original change rules of the templates were optimized and reconstructed.The BP neural network algorithm was used to establish the waist and hip circumference size changes. By kirectly using the matching of data and tata,the parametric design of men's waistband, abdomen and hip modification rules was realized. In other words, in a given amount of waist and hip circumference, a corresponding change rule can be acquired. By calling the chaneg rule a sample coming in line with the size will the size will be automatically achived, initially realizing one person and one board. The clothing fitting is improved and the dependence on modelers is reduced.