Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (09): 186-191.doi: 10.13475/j.fzxb.20181001406

• Management & Information • Previous Articles     Next Articles

Prediction of relationship between shoulder strap attribute and breast amplitude of sports bra by BP neural network

ZHOU Jie(), MA Qiurui   

  1. School of Apparel and Art Design, Xi'an Polytechnic University, Xi'an, Shaanxi 710048
  • Received:2018-10-09 Revised:2019-06-11 Online:2019-09-15 Published:2019-09-23

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Abstract:

In order to determine the influence of 3 properties of the sports bras shoulder straps on breast amplitudes during running, the motions of breast markers were attached in 6 different left breast positions of 8 subjects, and different shoulder straps were replaced for the human motion testing. The 3-D coordinates of the breast markers were recorded, and the amplitudes of the breast motion were obtained. The weight relationship between the 3 properties of the shoulder straps and the breast amplitude was determined by using the BP neural network model to replace different model parameters. The results show that when the transmission function of BP neural network is tansig function, the number of implicit layer neurons is 21, and the training function is traingdm as the network parameters, the breast amplitude values of the network fitting is close to the true values of the breast motion to 99.44%. The weights and thresholds of the network input to the hidden layer and the hidden to the output layer are respectively obtained under the network parameters. The positive inference relationship between 3 kinds of properties of the shoulder straps and the breast amplitudes can be obtained.

Key words: sports bra, bra shoulder strap, BP neural network, breast amplitude

CLC Number: 

  • TS941.763.5

Fig.1

Shoulder straps"

Fig.2

10 test markers"

Tab.1

Sample measurement values for marker points"

标记点
序号		 距离/
cm		 面密度/
(g·m-2)		 伸长
率/%		 强力/
N		 每个标记点整体振幅/cm
x方向 y方向 z方向
1 7.18 66.3 133 0.553 0.45 1.13 0.23
2 4.31 51.3 82 1.036 0.40 0.66 0.23
3 6.20 73.8 80 1.458 0.47 0.77 0.21
? ? ? ? ? ? ? ?
431 5.78 59.4 74 1.462 1.69 1.63 0.96
432 6.68 69.4 81 1.570 1.74 1.91 1.10

Fig.3

BP Neural network topology graph"

Tab.2

Mean square error of 60 kinds of network net"

隐含层
神经元个数		 logsig下的训练函数 tansig下的训练函数
trainbfg函数 traingd函数 traingdm函数 trainbfg函数 traingd函数 traingdm函数
20 net1(0.078) net2(0.053) net3(0.045) net4(0.045) net5(0.033) net6(0.025)
21 net7(0.096) net8(0.066) net9(0.040) net10(0.040) net11(0.036) net12(0.023)
22 net13(0.089) net14(0.077) net15(0.047) net16(0.047) net17(0.037) net18(0.025)
23 net19(0.072) net20(0.087) net21(0.048) net22(0.048) net23(0.037) net24(0.027)
24 net25(0.078) net26(0.055) net27(0.048) net28(0.048) net29(0.045) net30(0.026)
25 net31(0.095) net32(0.059) net33(0.048) net34(0.045) net35(0.039) net36(0.029)
26 net37(0.080) net38(0.062) net39(0.049) net40(0.049) net41(0.030) net42(0.032)
27 net43(0.097) net44(0.088) net45(0.059) net46(0.059) net47(0.038) net48(0.027)
28 net49(0.096) net50(0.082) net51(0.052) net52(0.052) net53(0.032) net54(0.026)
29 net55(0.089) net56(0.083) net57(0.059) net58(0.049) net59(0.038) net60(0.027)

Fig.4

BP neural network fitting transmission diagram"

Tab.3

Final weights and thresholds of network"

行列序号 输入层到隐含层 隐含层到输出层
权值矩阵iw1 阈值矩阵b1 权值矩阵iw2的转置 阈值矩阵b2
第1列 第2列 第3列 第4列 第1列 第1列 第2列 第3列 第1列
第1行 -26.17 56.67 -2.03 111.23 13.63 -0.47 -0.52 -0.41 1.10
第2行 2.28 -4.50 -0.01 0.66 -12.98 1.21 3.10 2.05 0.95
第3行 2.03 -58.88 0.39 17.01 -15.79 1.11 1.26 0.71 0.42
第4行 -0.41 -14.59 -0.15 -5.35 41.85 5.64 9.81 7.02
第5行 -3.32 -6.20 -0.16 -5.45 47.19 0.97 1.22 0.67
第6行 -6.78 -1.97 -0.59 -15.81 130.42 -0.32 -0.47 -0.34
第7行 -7.00 6.39 -0.15 -7.87 61.04 -0.61 -0.65 -0.48
第8行 0.32 11.22 0.07 1.68 -23.98 -5.09 -9.73 -6.76
第9行 -11.57 31.61 1.77 12.12 -122.90 0.66 0.98 0.53
第10行 -30.91 97.42 0.23 70.22 -63.00 -0.23 -0.32 -0.24
第11行 -1.48 4.75 -0.01 -1.28 8.89 1.93 4.04 2.63
第12行 -4.80 -46.03 0.30 -9.72 68.04 -0.81 -1.03 -0.60
第13行 -6.21 41.80 1.06 34.48 -145.52 -0.88 -1.12 -0.64
第14行 -51.15 -28.91 3.91 -54.67 -6.12 0.48 0.72 0.35
第15行 -0.36 -12.74 -0.10 -3.35 31.93 -10.30 -18.57 -13.07
第16行 -7.53 28.20 -0.20 20.64 -18.45 -0.63 -0.91 -0.45
第17行 -19.96 -11.37 0.33 29.72 46.60 0.59 0.68 0.43
第18行 -5.40 23.37 0.24 4.58 -26.83 0.98 1.37 0.79
第19行 40.27 -3.86 -0.01 -0.17 -225.58 0.09 0.51 0.07
第20行 -18.74 15.71 0.02 1.22 71.45 -1.63 -2.04 -1.21
第21行 7.02 -4.53 0.72 -31.33 -39.55 -0.77 -1.01 -0.58
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