基于GRA-GA-BP神经网络的家居服面料透气性能预测  

Prediction of breathability performance of household apparel based on GRA-GA-BP neural network

作　　者：王彬霞 王春红[1,2,3] 陈雅颂 周金香 殷兰君 杨道鹏 WANG Binxia;WANG Chunhong;CHEN Yasong;ZHOU Jinxiang;YIN Lanjun;YANG Daopeng(School of Textile Science and Engineering,Tiangong University,Tianjin 300387,China;Tianjin and Education Ministry Key Laboratory of Advanced Textile Composite Materials,Tiangong University,Tianjin 300387,China;Key Laboratory of Hollow Fiber Membrane Materials and Membrane Processes,Tiangong University,Tianjin 300387,China;School of Mathematical Sciences,Tiangong University,Tianjin 300387,China;Shenzhen Purcotton Co.,Ltd.,Shenzhen 518109,China;Shaoxing Zhongfanglian Inspection Technology Service Co.,Ltd.,Shaoxing 312000,China)

机构地区：[1]天津工业大学纺织科学与工程学院,天津300387 [2]天津工业大学教育部与天津市共建先进复合材料重点实验室,天津300387 [3]天津工业大学中空纤维膜材料与膜过程重点实验室,天津300387 [4]天津工业大学数学科学学院,天津300387 [5]深圳全棉时代科技有限公司,深圳518109 [6]绍兴中纺联检验技术服务有限公司,浙江绍兴312000

出　　处：《丝绸》2024年第10期46-52,共7页Journal of Silk

基　　金：国家自然科学基金项目(52203276)。

摘　　要：本文构建了一种改进BP神经网络模型来预测家居服面料的透气性能,能为家居服设计提供重要的参考。首先,采用灰色关联分析法(Grey Relation Analysis,GRA),选择与透气率关联度较大的因素作为研究对象。其次,采用遗传算法(GA)优化BP神经网络的结构参数,构建基于灰色关联分析的遗传算法优化BP(GRA-GA-BP)神经网络预测模型。选取58种面料成分不同、织物组织各异的家居服面料,其中42种为模型训练样本,16种为测试样本对建立的模型进行验证。实验结果表明,透气率实测值与预测值平均相对误差为8.39%;对透气率实测值与预测值进行相关性分析,拟合优度R^(2)为0.976。研究表明,该预测模型预测效果良好、预测精度高,在一定程度上可以精准预测家居服面料的透气率。With the improvement of people’s living standards,people have higher requirements for the comfort of household apparel.Breathability is one of the key factors affecting the comfort of household apparel and is the most concerned by household apparel consumers.At present,research on the comfort of household apparel is still in a blank period both domestically and internationally.There is a lack of research on the breathability of various household apparel fabrics with different fabric compositions and textures,and there is relatively little research on predicting the comfort of household apparel.Based on this,this article selects 58 common household apparel fabrics with different fabric compositions and textures on the market,and constructs a genetic algorithm improved BP neural network model to predict the breathability performance of household apparel.Firstly,to study the relationship between various influencing factors and air permeability of household apparel fabrics,the grey relational analysis(GRA)method was used to analyze the degree of influence of each influencing factor on the air permeability of household apparel fabrics.The factors with higher correlation were selected as input parameters for the model in this study,namely density,yarn diameter,thickness,and weight.Secondly,due to the shortcomings of BP neural network,such as proneness to local minima,slow learning rate,and long training time,this study used genetic algorithm(GA)to optimize the structural parameters of BP neural network,and constructed a genetic algorithm optimized BP(GRA-GA-BP)neural network prediction model based on grey correlation analysis.Genetic algorithm can optimize the structural parameters of the model,find the best parameter combination,and solve complex and high-dimensional problems,without being affected by local optimal solutions.58 household apparel fabrics with different fabric compositions and textures were selected,of which 42 were model training samples and 16 were test samples to validate the established model.The

关 键 词：织物 家居服 灰色关联分析 改进BP神经网络 透气性预测 

分 类 号：TS101.923.4[轻工技术与工程—纺织工程]