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

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dc.contributor.authorCheng, Pengpeng
dc.contributor.authorWang, Jianping
dc.contributor.authorZeng, Xianyi
dc.contributor.authorBruniaux, Pascal
dc.contributor.authorChen, Daoling
dc.contributor.organizationDonghua University, College of Fashion and Design, Shanghai, China
dc.contributor.organizationGemtex, Ensait, Centrale Lille,Roubaix, France
dc.contributor.organizationMinjiang University, Clothing and Design Faculty, Fuzhou, China
dc.date.accessioned2025-06-11T11:21:50Z
dc.date.available2025-06-11T11:21:50Z
dc.date.issued2022
dc.description.abstractIn 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.en
dc.description.sponsorshipThis paper was financially supported by the China Scholarship Council.
dc.identifier.citationCheng P, Wang J, Zeng X, Bruniaux P, Chen D. Intelligent Prediction Model of the Thermal and Moisture Comfort of the Skin-Tight Garment. FIBRES & TEXTILES in Eastern Europe 2022; 30, 1(151): 50-58. DOI: 10.5604/01.3001.0015.6461
dc.identifier.doi10.5604/01.3001.0015.6461
dc.identifier.issn1230-3666
dc.identifier.issn2300-7354
dc.identifier.urihttps://open.icm.edu.pl/handle/123456789/25959
dc.language.isoen
dc.publisherŁukasiewicz - Łódzki Instytut Technologiczny
dc.relation.ispartofseries30; 1
dc.rightsDozwolony użytek
dc.sourceFibres & Textiles in Eastern Europe
dc.subjectsportswear tightsen
dc.subjectthermal and moisture comforten
dc.subjectprincipal component analysisen
dc.subjectintelligent prediction modelen
dc.titleIntelligent Prediction Model of the Thermal and Moisture Comfort of the Skin-Tight Garmenten
dc.typearticle
dc.type.versionpublishedVersion
person.identifier.orcidCheng, Pengpeng [0000-0002-0055-5247]
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