基于MEA-BP神经网络的青年女性臂部体型识别  

Young female arm body shape recognition based on MEA-BP neural network

作　　者：倪世明[1] 白云龙 蒋益群 NI Shiming;BAI Yunlong;JIANG Yiqun(College of Architecture&Art Design,Shaoxing Vocational&Technical College,Shaoxing 312000,China;College of Information Engineering,Shaoxing Vocational&Technical College,Shaoxing 312000,China;College of Engineering,Iowa State University,Iowa 50011,USA)

机构地区：[1]绍兴职业技术学院建筑与设计艺术学院,浙江绍兴312000 [2]绍兴职业技术学院信息工程学院,浙江绍兴312000 [3]艾奥瓦州立大学工程学院,美国艾奥瓦州50011

出　　处：《丝绸》2022年第5期42-49,共8页Journal of Silk

基　　金：浙江省教育厅一般科研项目(Y202045029);浙江省教育厅教师专业发展项目(FX2019091);绍兴职业技术学院轻纺专项项目(SZK202035)。

摘　　要：青年女性臂部体型包含了大量的非线性特征,单一的BP神经网络很难达到理想预测精度,为快速准确地识别青年女性臂部体型,提高预测精度,本文构建了一种基于思维进化算法(MEA)优化BP神经网络的青年女性臂部体型识别模型。首先,通过[TC]^(2)三维人体测量获取611名青年女性的臂部数据;其次,通过主成分因子分析得到影响青年女性臂部体型特征的5大形态因子,选取5个特征指标采用两步聚类法将臂部体型分为4类;最后使用Matlab软件构建基于MEA-BP神经网络的青年女性臂部体型识别模型。实验结果显示:该模型能有效识别臂部体型,准确率为95.45%,与单一BP神经网络和GA-BP神经网络对比,该模型具有更高的预测精度和更优的非线性映射能力。The research on human arm shape is the key to improving the fit and comfort of the sleeve.Young females’ arm shape has multiple nonlinear features.Since the initial threshold and weight are randomly selected,the single BP neural network is prone to the problems of unstable model and low accuracy when dealing with the complex nonlinear relationship of human body shape.The thought evolution algorithm is a global optimization algorithm,which can optimize the initial threshold and weight of BP neural network through multiple "convergence" and "alienation" operations,so as to improve the stability of the model and prediction accuracy.In order to quickly and accurately identify a young female arm shape,this paper constructs a MEA optimized BP neural network based arm shape recognition model for young females.Firstly,the arm data of 611 young women aged 18-25 are obtained by [TC]^(2)3 D body measurement,and the values of 14 measurement items are extracted.SPSS software is used to preprocess the data,and descriptive statistical analysis of the indicators is conducted.Secondly,through principal component factor analysis,five morphological factors affecting young female arm shape are obtained,namely,arm circumference factor,arm root factor,arm root morphology factor,arm length factor and arm proportion factor.The characteristic factors corresponding to the maximum load of the five factors are:upper arm circumference,root circumference,root flattening rate,whole arm length and upper arm length/forearm length,which are selected and divided into five categories by two-step clustering method:long fat arm,middle arm,long thin arm and short flat arm,accounting for 17.4%,30.7%,22.4% and 29.5%,respectively.Finally,Matlab software is used to construct a young female arm body shape recognition model based on MEA-BP neural network.MEA optimizes the weight and threshold value of BP neural network,which promotes the learning process of the whole network and improved the stability and recognition accuracy of the model.The innovation o

关 键 词：青年女性 臂部体型 体型分类 MEA-BP神经网络 识别模型 

分 类 号：TS941.17[轻工技术与工程—服装设计与工程]