关键词: 短裤, 特征截面, RBF神经网络, 曲线拟合, Matlab仿真

Abstract:

Three-dimensional body scanning technique is used to collect point clouds data from the dressed mannequin and capture the shorts’ typical cross section that is corresponding to the feature points of body. By change the original coordinate point to polar angle and polar radius under the polar coordinate system, and taking the polar angle as the input and the polar radius as the output, RBF neural network model of the shorts’ typical cross section is established. Then the curve of clothing typical cross section is fitted and the fitting effect is compared with that of BP, least square method and cubic splines. The experiment results show that the mean average absolute percentage error of both neural networks is less than that of least square method and cubic splines. The simulation output curve is very close to original data and the curve is smooth. RBF network has much higher training speed, fewer training steps. and fitting efficiency superior to the BP neural network.

Key words: shorts, typical cross section, RBF neural network, curve fitting, Matlab simulation