Research on underwear pressure prediction based on improved GA-BP algorithm

Abstract

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.

Citation

Cheng, P., Chen, D. and Wang, J. (2021), "Research on underwear pressure prediction based on improved GA-BP algorithm", International Journal of Clothing Science and Technology, Vol. 33 No. 4, pp. 619-642. https://doi.org/10.1108/IJCST-05-2020-0078