基于遗传算法优化的BP神经网络对面料悬垂系数的预测及分析  

Prediction and analysis of fabric drape coefficient based on genetic-algorithm optimized BP neural network

作　　者：邢昊 张瑞云[1,2] 许腾飞 纪峰[1,2] XING Hao;ZHANG Ruiyun;XU Tengfei;JI Feng(College of TextileMinistry of Education,Donghua University,Shanghai 201620,China;Key Laboratory of Textile Science and Technology,Ministry of Education,Donghua University,Shanghai 201620,China)

机构地区：[1]东华大学纺织学院,上海201620 [2]东华大学纺织面料技术教育部重点实验室,上海201620

出　　处：《山东科学》2025年第1期53-63,共11页Shandong Science

摘　　要：通过对面料悬垂系数的精确预测,实现面料悬垂性虚拟化的初步研究。回归分析等方法虽实现了部分悬垂指标的预测,但其存在预测准确性不高,部分指标无法计算的问题。为此,提出了一种基于遗传算法优化BP神经网络(GA-BP神经网络)的新方法,从面料数据库中选取100块纯棉机织面料样本,其中训练样本80块,测试与验证集各10块,通过遗传算法优化神经网络的参数,采用相关性分析优化样本输入参数,以此提高模型的预测能力。10块测试样的悬垂系数预测结果表明,与传统BP神经网络相比,GA-BP神经网络平均绝对百分比误差从12.74%降到了7.03%,同时,利用经验公式判断误差循环获取了最佳的隐含层节点数为9。研究表明,GA-BP神经网络能够有效提升面料悬垂性预测的准确度,对于面料悬垂性的虚拟化表现具有重要的应用价值。Although regression analysis can predict some drape indicators,they have problems such as low prediction accuracy and the inability to calculate some indicators.To overcome these issues,this study proposes a new method using genetic algorithm to optimize BP neural network(GA-BP neural network)to improve the prediction accuracy of real fabric drape.In this study,we designed a GA-BP neural network model,selected 100 pure cotton woven fabric samples from the fabric database,including 80 training samples,10 test samples,and 10 validation samples,used the genetic algorithm to optimize the parameters of the neural network,and used correlation analysis to optimize sample input parameters to improve the prediction performance of the model.The results of the drape coefficient prediction for the 10 test samples show that compared with the traditional BP neural network,the average absolute percentage error of the BP neural network optimized by the genetic algorithm decreased from 12.74%to 7.03%.Furthermore,we used an empirical equation to identify error cycles and concluded that the optimal number of hidden layer nodes is 9.This study indicates that the GA-BP neural network can effectively improve the accuracy of fabric drape prediction and has important application value for the virtualization of fabric drape performance.

关 键 词：悬垂系数 面料数据库 神经网络 遗传算法 

分 类 号：TS10[轻工技术与工程—纺织工程]