# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
###### Abstract  
With the increasing demand for intelligent custom clothing, the development of highly accurate human body dimension prediction tools using artificial neural network technology has become essential to ensuring high-quality, fashionable, and personalized clothing. Although support vector regression (SVR) networks have demonstrated state-of-the-art (SOTA) performances, they still fall short on prediction accuracy and computation efficiency. We propose a novel generalized regression forecasting network (GRFN) that incorporates kernel ridge regression (KRR) within a multi-strategy multi-subswarm particle swarm optimizer (MMPSO)-SVR nonlinear regression model that applies a residual correction prediction mechanism to enhance prediction accuracy for body dimensions. Importantly, the predictions are generated using only a few basic body size parameters from small-batch samples. The KRR regression model is employed for preliminary residual sequence prediction, and the MMPSO component optimizes the SVR parameters to ensure superior correction of nonlinear relations and noise data, thereby yielding more accurate residual correction value predictions. The GRFN hybrid model is superior to SOTA SVR models and increases the root mean square performance by 91.73-97.12% with a remarkably low mean square error of 0.0054 \(\pm\) 0.07. This outstanding advancement sets the stage for marketable intelligent apparel design tools for the fast fashion industry. hybrid model; residual correction; support vector regression (SVR); multi-swarm PSO; body dimension prediction +
Footnote †: journal:  
0000-0001-889-2233-223  
features, including the thickness/width parameters of cross-sections, with the objective of constructing additional lower-body characteristics. [PERSON] and [PERSON] [4] used the U.S. size database to train a new lasso regression method that establishes relationships between key predictor variables related to croth length to improve the fit of bifurcated garments. [PERSON] et al. [5] utilized 3D anthropometric data to forecast the relevant parameters in shirt pattern design by deploying an ANN with a linear regression model to reveal the relationship between patterns and body measurements. However, the nonlinearity in the relationships between different body parts becomes more pronounced, and the MLR model struggles to accurately capture this characteristic. [PERSON] et al. [6] provided a back-propagation (BP) ANN that uses anthropometric data to predict human body dimensions, providing robust support for custom clothing. Furthermore, [PERSON] et al. [7] introduced digital clothing pressure from virtual try-ons as input parameters and employed a BPNN model to predict the fit of clothing, including tight, well-fitted, or loose. This research has expanded the potential applications of BPNN methods in the field of clothing design. However, due to the random selection of initial weights and thresholds in BPNN, the network may become stuck in local minima, thereby diminishing its fitting performance. To overcome this challenge, [PERSON] et al. [8] employed a genetic algorithm to implement a GA-BP-K-means model to cluster data and enhance body shape prediction. This method involves using a genetic algorithm to optimize the initial parameters of the BPNN. Similarly, [PERSON] et al. [9] addressed the issue of predicting underwater pressure using an improved GA algorithm combined with a BPNN model. The improved GA algorithm accelerated the BP neural network's convergence speed and enhanced underwater pressure prediction accuracy. In addition to BPNN, other ANN models, such as the radial basis function neural network (RBF-NN) are also often used to estimate body sizes. For example, [PERSON] et al. [10] trained a model on clothing knowledge to extract key human body feature parameters using factor analysis with a combined RBF-NN and linear regression method. This enabled the prediction of detailed human body dimensions with a small number of feature parameters. [PERSON] et al.