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.

翻译：神经网络模型在各领域展现了卓越性能及对复杂问题的成功解决，但多数模型被视为黑箱，需大量数据支撑开发。因此，在数据有限的情况下，由于缺乏透明性和数据稀缺，构建合适模型面临挑战。针对这些问题，本研究提出采用灰色信息神经网络（GINN）。该网络确保输出遵循灰色系统的微分方程模型，从而提升可解释性。此外，融入灰色系统理论的先验知识，使传统神经网络能有效处理小样本数据。实验表明，所提模型能够揭示真实世界中的潜在模式，并基于经验数据生成可靠预测。