Clustering of the body shape of the adult male by using principal component analysis and genetic algorithm–BP neural network

Abstract

In order to improve the efficiency and accuracy of human body shape prediction, principal component analysis method (PCA) is proposed to reduce the dimension of related variables and eliminate the multicollinearity among variables. Then, the transformed variables are input into genetic algorithm and BP neural network, and a new method of human body shape prediction is designed. To avoid the problems that slow convergence speed and easy falling into local minima of BP neural network, the genetic algorithm is used to optimize the weights and thresholds of BP neural network. Moreover, to prove the superiority of PCA–GA–BP model, the prediction results are compared with those of other algorithms. Body sizes of 18–25-year-old, 26–44-year-old and 45–59-year-old males were selected as experimental data to analyze these models. The prediction results of GA–BP, PCA–BP, BP, SVM and K-means were compared with PCA–GA–BP neural network. The results show that the prediction effect of PCA–GA–BP neural network is significantly better than that of GA–BP, PCA–BP, BP, SVM and K-means prediction models, which can accurately predict and cluster the human body shape. The model has better prediction and classification and simpler structure.