Neural network models have shown outstanding performance and successful
resolutions to complex problems in various fields. However, the majority of
these models are viewed as black-box, requiring a significant amount of data
for development. Consequently, in situations with limited data, constructing
appropriate models becomes challenging due to the lack of transparency and
scarcity of data. To tackle these challenges, this study suggests the
implementation of a grey-informed neural network (GINN). The GINN ensures that
the output of the neural network follows the differential equation model of the
grey system, improving interpretability. Moreover, incorporating prior
knowledge from grey system theory enables traditional neural networks to
effectively handle small data samples. Our proposed model has been observed to
uncover underlying patterns in the real world and produce reliable forecasts
based on empirical data.

本研究提出了一种灰色信息驱动的神经网络模型（GINN），使神经网络的输出遵循灰色系统的微分方程模型，提高解释性。此模型结合了灰色系统理论的先验知识，能够有效处理少量数据样本，发现真实世界中的潜在模式并基于经验数据产生可靠的预测。