Gülseren Karabay, Y. Senol, H. Ozturk, Cansu Mesegul
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引用次数: 0
摘要
摘要本研究的目的是通过人工神经网络(ANN)估计由易熔线和防粘缝线组成的接缝的防水性值。易熔线首次被用于获得防水接缝。因此,借助于根据测试值创建的模型来估计防水性变量的值,有助于加快进一步研究的进展。因此,为两种织物制备了十个不同的样品,并使用Textest FX 3000静水压头测试仪III测试所获得的接缝的防水性值。为了预测接缝的防水值,将Levenberg-Marquardt反向传播算法用于具有S形和正线性传递函数的人工神经网络模式模型。最后,人工神经网络模型成功地估计了煤层的防水性。相关系数最高为R=0.95081,表明神经网络模型的预测是可靠的。
Artificial Neural Network-based Prediction Technique for Waterproofness of Seams Obtained by Using Fusible Threads
Abstract The aim of this study was to estimate waterproofness values of seams composed of the combination of fusible threads and antiwick sewing threads through artificial neural networks (ANN). Fusible threads were used to obtain waterproof seams for the first time. Therefore, estimating the value of the waterproofness variable with the help of models created from test values can contribute to accelerating the progress of further studies. Hence, ten different samples were prepared for two fabrics, and the waterproofness values of the seams obtained were tested using a Textest FX 3000 Hydrostatic Head Tester III. For the prediction of waterproofness values of the seams, the Levenberg-Marquardt backpropagation algorithm was used for artificial neural network pattern models with sigmoid and positive linear transfer functions. Finally, the ANN model was successful in estimating the waterproofness of the seams. The highest correlation coefficient was R = 0.95081 which indicated that the prediction made by the neural network model proved to be reliable.
期刊介绍:
FIBRES & TEXTILES in Eastern Europe is a peer reviewed bimonthly scientific journal devoted to current problems of fibre, textile and fibrous products’ science as well as general economic problems of textile industry worldwide. The content of the journal is available online as free open access.
FIBRES & TEXTILES in Eastern Europe constitutes a forum for the exchange of information and the establishment of mutual contact for cooperation between scientific centres, as well as between science and industry.