A novel neural grey system model with Bayesian regularization and its applications

Abstract

Most machine learning models are essentially “Black-box” models, of which the performance heavily relies on large scale data sets. In this work the idea of “Grey-box” modelling is adopted in order to take most advantage of known information represented by deterministic structure, and then the neural grey system model is developed. Levenberg-Marquardt algorithm is used to train the proposed model, and the Bayesian regularization is used to tune the regularized parameter automatically. Six real world case studies are presented to show the performance of the proposed model, comparing to 6 existing machine learning models and 17 grey system models. The results show that the proposed model can significantly overperform the other models and has very good generality, illustrating its high potential in a wide variety of real world applications and the efficiency of the proposed modelling method.