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In order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure. [ABSTRACT FROM AUTHOR]

Purpose: For comfort evaluation of underwear pressure, this paper proposes an improved GA algorithm to optimize the weight and threshold of BP neural network, namely PSO-GA-BP neural network prediction model. Design/methodology/approach: The objective parameters of underwear, body shape data, skin deformation and other data are selected for simulation experiments to predict the objective pressure and subjective evaluation in dynamic and static state. Compared with the prediction results of BP neural network prediction model, GA-BP neural network prediction model and PSO-BP neural network prediction model, the performance of each prediction model is verified. Findings: The results show that the BP neural network model optimized by PSO-GA algorithm can accelerate the convergence speed of the neural network and improve the prediction accuracy of underwear pressure. Originality/value: PSO-GA-BP model provides data support for underwear design, production and processing and has guiding significance for consumers to choose underwear. [ABSTRACT FROM AUTHOR]

In order to study the influence of underwear on microclimate heat transfer among different age groups, this study measured the temperature of the microclimate layer corresponding to the main parts of the human body or the key parts that affect average skin temperature. A computational fluid dynamics numerical model was then used to simulate the influence of underwear on heat transfer between the human body and the microenvironment and to explore the physical phenomenon. The results obtained show that underwear has a great influence on the average temperature of the microclimatic air layer, especially the air layer at the upper arm, forearm, and thigh. The findings of this study provide fundamental knowledge to improve the thermal comfort of underwear. [ABSTRACT FROM AUTHOR]