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In order to study the comfort of tight-fitting sportswear in winter, this paper designed a series of motions, and explored the distribution of comfort perception under different sports conditions by evaluating the wearing perception of human body. Finally, through the acquired experimental data, intelligent prediction models were established, and the prediction results were visualized, which makes the comfort distribution more intuitive. The results show that there are great differences in the parts that affect the overall comfort perception under different sports conditions; different ages subjects have different perceptions of comfort; Particle Swarm Optimization-Cuckoo Search-Adaptive Network-based Fuzzy Inference System has better prediction accuracy, and could replace the wearing trials. [ABSTRACT FROM AUTHOR]

In order to understand consumers' cognition of clothing style and design clothing products more in line with people's emotional needs, a garment style perceptual image prediction model based on PSO-BP neural network was constructed by taking professional dress as an example. Firstly, the professional dress samples were screened and the style design elements were deconstructed and coded. The Kansei engineering theory and factor analysis method were used to determine the representative adjectives, so as to reduce the cognitive dimension of the target users for the style characteristics and perceptual image of the dress. Then, using the sample style design element code as the input layer and the user's perceptual image evaluation score as the output layer, the PSO-BP neural network's perceptual image prediction model for professional dress styles is constructed. Finally, the sample data were input into the PSO-BP model, BP neural network and GA-BP model for simulation and calculation, and the error analysis of the results proved that the PSO-BP prediction model is effective and advanced. Designers can use this model to quickly transform customers' perceptual needs with dress style design elements, so as to improve the scientificity of design decision-making and better meet customer needs. [ABSTRACT FROM AUTHOR]

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: The purpose of this paper is to improve the prediction accuracy of the body shape prediction model and provide some reference value for the design of underwear. Design/methodology/approach: The body size data of 250 male youths is measured to analyze the body shape of the lower body. And there is a total of 56 measurement items, which are clustered by GA-BP-K-means, K-means, optimal segmentation method for ordered samples, wavelet coefficient analysis, regression analysis and Naive Bayes Algorithm. Finally, a test male sample of an unknown body shape was clustered to verify the superiority of the GA-BP-K-means. Findings: This paper presented the key factors for body shape clustering, and experimental results have shown that the GA-BP neural network model is higher in speed and precision than other algorithm prediction models. Originality/value: It was clarified which is the key to body shape clustering. At the same time, the GA-BP-K-means algorithm can promote the popularization and application of the prediction model in body shape clustering. [ABSTRACT FROM AUTHOR]