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.

中文翻译：

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基于主成分分析和遗传算法-BP神经网络的内衣热湿舒适性预测模型研究

为了提高内衣热湿舒适性预测的效率和准确性，采用主成分分析法设计了一种新的预测模型，以减小相关变量的维数，消除变量之间的多重共线性关系，然后输入将变量转换成遗传算法（GA）和BP神经网络。为避免收敛速度慢，容易陷入BP神经网络局部最小值的问题，本文采用遗传算法对BP神经网络的权重和阈值进行优化，并利用MATLAB软件进行编程，建立了BP神经网络。 BP神经网络和GA–BP神经网络的预测模型。为了验证模型的优越性，将GA–BP，PCA–BP和BP的预测结果与GA–BP神经网络进行了比较。结果表明，PCA可以提高GA-BP神经网络在热和湿舒适度预测中的准确性和适应性。PCA–GA–BP模型明显优于GA–BP，PCA–BP，BP，SVM和K-means预测模型，该模型可以准确地预测内衣的保暖性和湿气舒适性。该模型具有更好的精度预测和更简单的结构。