BP神经网络在塑身内衣压力预测中的应用
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摘要
为预测人体在穿着塑身内衣时产生的压力,提出一种基于神经网络的预测方法。首先测试在4种不同姿势下26名女大学生穿着同一品牌的塑身内衣时,人体20个部位产生的压力值;然后用2种不同的BP神经网络工具箱函数,建立压力预测模型,并分析压力值与人体5个测量值之间的关系,最后检验并比较2种工具箱函数以及不同输入因素对压力的预测效果。结果表明:该预测方法无需复杂的建模计算过程,可直接利用人体的5个测量值较为准确地预测塑身内衣对人体产生的压力值; newff函数的预测效果优于feedforwardnet函数;对2种站姿时的预测精度优于2种坐姿;增加站姿的压力值作为预测输入因素,可准确预测其他3种姿势下的压力值,其预测精度达到82%以上。
In order to predict underwear pressure when wearing shapewear,a prediction method based on neural network was proposed. A total of 26 female college students wore the shapewear of the same brand,and were asked to perform four poses. The pressure values at 20 body points of each subject were measured,respectively. Two different BP neural network toolbox functions were used to establish the pressure prediction models. The relationships between the pressure values and five body measurements were analyzed. The prediction results were tested and compared while using the two different toolbox functions and different input values. The results show that the method based on neural network does not need complicated calculation process. By this method,five body measurement values can be directly used to predict the pressure value of shapewear. The prediction accuracy of the newff function is superior to that of the feedforwardnet function. The accuracy of the prediction of pressure in the two standing poses is better than that in the two sitting poses. When taking the pressure values of standing pose and five body measurements as input values,the pressure values of the other three poses can be predicted with the accuracy over 82%.
In order to predict underwear pressure when wearing shapewear,a prediction method based on neural network was proposed. A total of 26 female college students wore the shapewear of the same brand,and were asked to perform four poses. The pressure values at 20 body points of each subject were measured,respectively. Two different BP neural network toolbox functions were used to establish the pressure prediction models. The relationships between the pressure values and five body measurements were analyzed. The prediction results were tested and compared while using the two different toolbox functions and different input values. The results show that the method based on neural network does not need complicated calculation process. By this method,five body measurement values can be directly used to predict the pressure value of shapewear. The prediction accuracy of the newff function is superior to that of the feedforwardnet function. The accuracy of the prediction of pressure in the two standing poses is better than that in the two sitting poses. When taking the pressure values of standing pose and five body measurements as input values,the pressure values of the other three poses can be predicted with the accuracy over 82%.
引文
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[16]董君. MatLab语言的特点与应用[J].吉林省经济管理干部学院学报,2009,23(5):60-63.DONG Jun. The characteristics and application of MatLab language[J]. Journal of Jilin Province Economic ManagementCadreCollege,2009,23(5):60-63.
[17]蒲春,孙政顺,赵世敏. MatLab神经网络工具箱BP算法比较[J].计算机仿真,2006,23(5):142-144.PU Chun,SUN Zhengshun,ZHAO Shimin. Comparison of BP algorithm of MatLab NN toolbox[J]. Computer Simulation,2006,23(5):142-144.
[2]宋晓霞,冯勋伟.服装压力与人体舒适性之关系[J].纺织学报,2006,27(3):103-105.SONG Xiaoxia,FENG Xunwei. Relationship between garment pressure and human comfortableness[J].Journal of Textile Research,2006,27(3):103-105.
[3]孟祥令,张渭源.服装压力舒适性的研究进展[J].纺织学报,2006,27(7):109-112.MENG Xiangling,ZHANG Weiyuan. Progress of study on pressure comfort of clothing[J]. Journal of Textile Research,2006,27(7):109-112.
[4]徐军,陶开山,张莉,等.基于运动生物力学的女子沙滩排球服压力舒适性研究[J].西安工程大学学报,2009,23(2):454-460.XU Jun, TAO Kaishan, ZHANG Li, et al.Comfortableness of female beach volleyball clothes based on movement biological mechanic[J]. Journal of Xi'an Polytechnic University,2009,23(2):454-460.
[5]尹玲,张文斌,夏蕾.服装压力舒适性的研究综述与分析[J].纺织学报,2008,29(3):137-142.YIN Ling, ZHANG Wenbin, XIA Lei. Review and analysis of pressure comfort of garment[J]. Journal of Textile Research,2008,29(3):137-142.
[6]罗笑南,曾龙,聂卉.三维紧身内衣的压力分布计算模型[J].中山大学学报(自然科学版),2001,40(6):99-101.LUO Xiaonan,ZENG Long,NIE Hui. A computable model of pressure distribution of tight underwear[J].Acta Scientiarum Naturalium Universitatis Sunyatseni(Natural Science Edition),2001,40(6):99-101.
[7] WANG Jianmin,LUO Xiaonan, LI Yi, et al. The application of the volumetric subdivision scheme in the simulation of elastic human body deformation and garment pressure[J]. Textile Research Journal,2005,75(8):591-597.
[8] MIRJALILI S A,RAFFEEYAN M,SOLTANZADEH Z.The analytical study of garment pressure on the human body using finite elements[J]. Fibres&Textiles in Eastern Europe,2008,16(3):69-73.
[9] ZHANG X,YEUNG K W,LI Y. Numerical simulation of 3D dynamic garment pressure[J]. Textile Research Journal,2002,72(3):245-252.
[10]闵惜琳,刘国华.用MatLab神经网络工具箱开发BP网络应用[J].计算机应用,2001,21(8):163-164.MIN Xilin,LIU Guohua. Using MatLab neural network toolbox to develop BP network application[J].Computer Applications,2001,21(8):163-164.
[11]罗成汉.基于MatLab神经网络工具箱的BP网络实现[J].计算机仿真,2004,21(5):109-111.LUO Chenghan. Realization of BP network based on network tool kit in MatLab[J]. Computer Simulations,2004,21(5):109-111.
[12]沈波.基于BP神经网络的股价预测应用研究[D].长沙:湖南大学,2010:5-20.SHEN Bo. Research on the application of stock price forecasting based on BP neural network[D]. Changsha:Hunan University,2010:5-20.
[13]占辉.服装压力舒适性的人工神经网络预测[D].西安:西安工程大学2005:33-34.ZHAN Hui. Artificial neural network prediction of clothing pressure comfort[D]. Xi'an:Xi'an Polytechnic University,2005:33-34.
[14]孙宇,曾卫东,赵永庆,等.应用人工神经网络研究化学元素对钛合金相变点的影响[J].稀有金属材料与工程,2010,39(6):1031-1036.SUN Yu, ZENG Weidong, ZHAO Yongqing, et al.Study on effects of elloying elements onβtransus temperature of titanium alloys using artificial neural network[J]. Rare Metal Materials and Engineering,2010,39(6):1031-1036.
[15]郭利辉,周雅.基于MatLab神经网络工具箱的BP网络设计[J].信息技术与信息化,2009(3):20-22.GUO Lihui,ZHOU Ya. The design of BP network based on MatLab toolbox[J]. Information Technology&Informatization,2009(3):20-22.
[16]董君. MatLab语言的特点与应用[J].吉林省经济管理干部学院学报,2009,23(5):60-63.DONG Jun. The characteristics and application of MatLab language[J]. Journal of Jilin Province Economic ManagementCadreCollege,2009,23(5):60-63.
[17]蒲春,孙政顺,赵世敏. MatLab神经网络工具箱BP算法比较[J].计算机仿真,2006,23(5):142-144.PU Chun,SUN Zhengshun,ZHAO Shimin. Comparison of BP algorithm of MatLab NN toolbox[J]. Computer Simulation,2006,23(5):142-144.