基于BP神经网络的织物斜向弯曲性能的预测

Prediction of fabric diagonal bending rigidity by BP neural network

针对织物斜向弯曲刚度传统数学模型预测精度较低的实际情况,选择若干块织物沿纬向到经向每隔10°测试弯曲性能,应用基于梯度降算法的BP神经网络对织物斜向弯曲刚度进行预测,并用误差平方和作为指标进行检验和比较。结果表明:这种网络模型能够有效预测织物斜向任意角度的弯曲刚度;与传统的数学模型相比,该网络模型的精准度和泛化能力更高,可为织物斜向弯曲刚度的预测提供一种新的客观评价方法。

At present,the accuracy is very low in the prediction of fabric diagonal bending rigidity with mathematic model.Several pieces of fabric are selected and their bending rigidity in every 10 degrees from the weft-wise to warp-wise is tested,and prediction of diagonal bending rigidity of the fabrics are carried out by BP neural network based on gradient algorithm.And verification and comparison are performed.Experimental results demonstrate that the proposed system can efficiently predict fabric diagonal bending rigidity with better accuracy and offers a new evaluation method for prediction of fabric diagonal bending rigidity.