亚麻织物接枝率与透气率变化关系的数学模型

 摘 要 ： 本设计通过紫外光引发接枝丙烯酸对亚麻织物进行改性处理。应用BP神经网络法和最小二乘回归法分别对不同光照接枝时间、光引发剂用量、单体浓度下的织物接枝率与透气率变化量之间关系进行建模。将接枝率作为输入、透气率变化量作为输出，通过讨论确定神经网络结构为1-10-1，S型函数作为激活函数；同时选出最优网络参数即迭代次数100、训练目标0.001。BP 网络模型与最小二乘模型相比，仿真输出与目标输出相关系数高，误差百分比小。因此，BP神经网络模型具有更好的仿真精度，为接枝率和透气率间关系的探索提供了一种准确有效的预测模型。

Mathematical model for relationship between grafting ratio and variation of air permeability of linen fabric

Abstract Modification of linen fabric is performed via UV initiated photografting of acrylic acid in this design. BP neural network and least squares regression modeling methods are used to predict the relationship between grafting ratio and air permeability under different conditions of photografting time, photoinitiator amount and concentration of acrylic acid, respectively. A three-layer BP network model with architecture of 1-10-1 is established after extensive discussion, including one node in one input layer representing grafting ratio, one node in one output layer representing variation of air permeability and ten nodes in one hidden layer. The activation function of sigmoid is selected. The optimum parameters, training step of 100 and training goal of 0.001, are determined. The correlation coefficient of BP neural network model is higher than least squares regression model, while the percentage error is lower. Therefore, BP neural network has higher simulation precision, which provides an effective predictive model for the relationship between graft ratio and air permeability.