Recursive surrogate model based on generalized regression neural network

Abstract

Surrogate models have attracted considerable interest as approximation tools that can save considerable computational resources in various applications. In this study, a recursive surrogate model was developed based on a generalized regression neural network and variable-fidelity surrogate method. The proposed model can continuously improve its prediction accuracy using a novel recursive correction method. Ultimately, a model with sufficient predictive accuracy can be obtained. To verify the performance of the proposed model, we conducted a series of comparative experiments using test functions and an engineering problem. The results showed that the proposed model has better predictive accuracy and robustness than the other benchmark models. Additionally, the impacts of the stopping criteria and spread factor on the performance of the proposed model were investigated, and the time cost associated with the modeling process was analyzed. This model presents a novel option for engineering design optimization. The recursive correction method also provides a new approach for other regression models to further improve their prediction accuracy.