A brief introduction to the Grey Machine Learning

TL;DR

This paper introduces Grey Machine Learning (GML), a kernel-based framework extending grey system models with nonlinear capabilities, highlighting its advantages over traditional grey models and LSSVM.

Contribution

The paper develops a kernel-based nonlinear extension of grey models, integrating LSSVM concepts to enhance grey system modeling.

Findings

Kernel implicit mapping estimates nonlinear functions in GML.

GML extends traditional grey models with nonlinear capabilities.

Discussion on GML's advantages over existing models.

Abstract

This paper presents a brief introduction to the key points of the Grey Machine Learning (GML) based on the kernels. The general formulation of the grey system models have been firstly summarized, and then the nonlinear extension of the grey models have been developed also with general formulations. The kernel implicit mapping is used to estimate the nonlinear function of the GML model, by extending the nonparametric formulation of the LSSVM, the estimation of the nonlinear function of the GML model can also be expressed by the kernels. A short discussion on the priority of this new framework to the existing grey models and LSSVM have also been discussed in this paper. And the perspectives and future orientations of this framework have also been presented.