年間 4 号発行
ISSN 印刷: 2151-4798
ISSN オンライン: 2151-562X
Indexed in
A NEURAL NETWORK SYSTEM FOR PREDICTION OF THERMAL RESISTANCE OF KNIT FABRICS
要約
An artificial neural network (ANN) was developed to predict the thermal resistance of knit fabrics. Thickness, porosity, air permeability, weight per unit area, and fiber conductivity were taken as input variables of the ANN. Data on thermal resistance were measured on experiments carried out on jersey knitted structures. An original (virtual leave-one-out) approach dealing with the overfitting phenomenon and allowing the selection of the optimal neural network architecture was used. The optimal ANN model contained three hidden neurons in the hidden layer. This model was validated by testing data, and the confidence intervals on the predictions were evaluated. It shows good performance in prediction with better accuracy. The developed neural model is expected to be used for a wide industrial context.
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Alibi Hamza, Fayala Faten, Jemni Abdelmajid, Zeng Xianyi, A neural network system for designing new stretch fabrics, 2013 International Conference on Electrical Engineering and Software Applications, 2013. Crossref
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Baghdadi Rania, Alibi Hamza, Fayala Faten, Zeng Xianyi, Investigation on Stiffness of Finished Stretch Plain Knitted Fabrics Using Fuzzy Decision Trees and Artificial Neural Networks, Fibers and Polymers, 22, 2, 2021. Crossref