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Article Dans Une Revue Fibres and Textiles in Eastern Europe Année : 2022

Intelligent Prediction Model of the Thermal and Moisture Comfort of the Skin-Tight Garment

Résumé

In order to improve the efficiency and accuracy of predicting the thermal and moisture comfort of skin-tight clothing (also called skin-tight underwear), principal component analysis(PCA) is used to reduce the dimensions of related variables and eliminate the multicollinearity relationship among variables. Then, the optimized variables are used as the input parameters of the coupled intelligent model of the genetic algorithm (GA) and back propagation (BP) neural network, and the thermal and moisture comfort of different tights (tight tops and tight trousers) under different sports conditions is analysed. At the same time, in order to verify the superiority of the genetic algorithm and BP neural network intelligent model, the prediction results of GA-BP, PCA-BP and BP are compared with this model. The results show that principal component analysis (PCA) improves the accuracy and adaptability of the GA-BP neural network in predicting thermal and humidity comfort. The forecasting effect of the PCA-GA-BP neural network is obviously better than that of the GA-BP, PCA-BP, BP model, which can accurately predict the thermal and moisture comfort of tight-fitting sportswear. The model has better forecasting accuracy and a simpler structure.
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Dates et versions

hal-04514693 , version 1 (21-03-2024)

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Pengpeng Cheng, J. P. Wang, Xianyi Zeng, Pascal Bruniaux, D. L. Chen. Intelligent Prediction Model of the Thermal and Moisture Comfort of the Skin-Tight Garment. Fibres and Textiles in Eastern Europe, 2022, Fibres and Textiles in Eastern Europe, 30, ⟨10.5604/01.3001.0015.6461⟩. ⟨hal-04514693⟩

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