Prediction of yarn crimp in PES multifilament woven barrier fabrics using artificial neural network
Prediction of yarn crimp in PES multifilament woven barrier fabrics using artificial neural network
复制标题
DOI：
10.1080/00405000.2017.1393786
复制
发表时间：
2018-01-01
影响因子：
1.7
通讯作者：
Cherif, Chokri
中科院分区：
文献类型：
--
作者：
Malik, Samander Ali;Gereke, Thomas;Cherif, Chokri
本研究旨在开发人工神经网络 (ANN) 模型来预测机织阻隔织物中的纱线卷曲。对于 ANN 培训，通过改变纬纱和长丝细度、纱线类型、纬纱密度、编织类型和织机参数生产 52 种聚酯 (PES) 复丝阻隔织物。神经网络的监督训练是使用 Matlab (R) ANN 工具箱函数 trainbr' 进行的，该函数将 Levenberg-Marquardt (LM) 优化和自动贝叶斯正则化纳入反向传播。从建模结果来看，经纱和纬纱卷曲模型都具有良好的泛化能力，在新数据上进行测试时具有出色的确定系数和微不足道的平均绝对误差。此外，优化网络的输入等级分析提供了关于输入变量的模型稳定性的重要信息，趋势分析阐明了使用不同输入水平的输入压接行为。
This research was aimed to develop artificial neural network (ANN) models to predict yarn crimp in woven barrier fabrics. For ANN training, 52 polyester (PES) multifilament barrier fabrics were produced by varying weft yarn and filament fineness, yarn type, weft density, weave type, and loom parameters. The supervised training of neural network was performed using Matlab (R) ANN toolbox function trainbr' which is the incorporation of Levenberg-Marquardt (LM) optimization and automated Bayesian regularization into backpropagation. From modeling outcomes, it was observed that both warp and weft yarn crimp models have generalized well with excellent coefficient of determination and trivial mean absolute error when tested on novel data. Moreover, input rank analysis of optimized network provided important information about model stability with respect to input variables, and trend analysis elucidated the input-crimp behavior using different input levels.