# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 5 Discussion and Conclusions  
With the rapid development of artificial intelligence technology, efficiently estimating the human body dimensions required for clothing patterns using ANN technology, rather than anthropometry measurement methods, has emerged as a new trend. The KRR regression model does not require feature selection and exhibits strong robustness to outliers, making it particularly suitable for handling datasets with noise or outliers. However, due to the ambiguity in the variable relationships among body dimensions, the KRR-based model may not fully capture their nonlinear features. Therefore, by enhancing the improved SVR model to compensate for the nonlinear features in the body dimension data, we have developed a nonlinear hybrid prediction approach with a high tolerance for input errors. This study employed an innovative data-driven GRFN hybrid model, based on a small-batch sample, for predicting highly accurate human body dimensions. We use the KRR regression model to fit and construct the predictive residual sequence. The MMPSO-SVR model is used to deal with nonlinear relationships and noisy data and to obtain more accurate predictive residual correction values. We then combine the estimated values from the KRR regression and the predictive residual correction values to obtain high-precision estimates of human body size parameters. Using the proposed teaching and learning co-optimization MMPSO algorithm enhances global search capabilities and population diversity [56], effectively preventing the algorithm from falling into local optima, thus leading to optimal SVR hyper-parameters. The experimental results validate the suitability of the proposed hybrid residual correction model, and compared with the performance of the single direct MMPSO-SVR model, the GRFN hybrid model exhibits astonishing improvements in RMSE of between 91.73% and 97.12%. Compared with the prediction accuracy and reliability results of other SOTA ANNs, those of our model are improved by 0.0054 \(\pm\) 0.07 in terms of MSE and SE. The absolute error fluctuation range of the hybrid model is smaller than those of the other models. Hence, this remarkable advancement provides the fashion industry with intelligent garment design tools effectively achieve the accuracy and efficiency of mass customization.  
To further improve prediction accuracy and ease of implementation, we plan to overcome data limitations and improve the anthropometric dataset to cover full-body information, especially the anthropometric data of abnormal body shapes. Apart from the sample data capacity, we will also consider the impact of gender, age, ethnicity, geographical region, and body mass index on data sources. We also plan to improve the diversity of enhanced particle individuals in the MMPSO algorithm by leveraging the topological structure and communication methods of particles to improve global search stability and convergence.  
Conceptualization, C.B. and Y.M.; methodology, C.B., Y.M. and X.Z.; software, C.B. and X.Z.; validation, C.B., J.C. and X.Z.; data curation, C.B.; writing--original draft preparation, C.B. and Y.M.; writing--review and editing, C.B., Y.M. and X.Z.; visualization, C.B.; supervision, Y.M.; project administration, Y.M. and X.Z.; funding acquisition, Y.M., J.C. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (grant No. 61972458, 62172367), the Zhejiang Provincial Natural Science Foundation of China (grant No. LZ23F020002), and the Zhejiang Province Public Welfare Technology Application Research Project (grant No. LGF22F020006).  
Not applicable.  
The datasets analyzed in the current study are available at: [[https://doi.org/10.1080/00405000.2017.1315794](https://doi.org/10.1080/00405000.2017.1315794)]([https://doi.org/10.1080/00405000.2017.1315794](https://doi.org/10.1080/00405000.2017.1315794)) (accessed on 1 August 2023).  
The authors declare no conflict of interest.