Purpose

In this study, a new neural differential grey model is proposed for the purpose of accurately excavating the evolution of real systems.

Design/methodology/approach

For this, the proposed model introduces a new image equation that is solved by the Runge-Kutta fourth order method, which makes it possible to optimize the sequence prediction function. The novel model can then capture the characteristics of the input data and completely excavate the system's evolution law through a learning procedure.

Findings

The new model has a broader applicability range as a result of this technique, as opposed to grey models, which have fixed structures and are sometimes over specified by too strong assumptions. For experimental purposes, the neural differential grey model is implemented on two real samples, namely: production of crude and consumption of Cameroonian petroleum products. For validation of the new model, results are compared with those obtained by competing models. It appears that the precisions of the new neural differential grey model for prediction of petroleum products consumption and production of Cameroonian crude are respectively 16 and 25% higher than competing models, both for simulation and validation samples.

Originality/value

This article also takes an in-depth look at the mechanics of the new model, thereby shedding light on the intrinsic differences between the new model and grey competing models.

中文翻译：

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灰色神经常微分方程预测模型学习潜在动力学及其应用

目的

本研究提出了一种新的神经差分灰色模型，旨在准确挖掘真实系统的演化过程。

设计/方法论/途径

为此，所提出的模型引入了一个新的图像方程，该方程通过龙格-库塔四阶方法求解，这使得优化序列预测函数成为可能。然后，新颖的模型可以捕获输入数据的特征，并通过学习过程完全挖掘系统的演化规律。

发现

由于这种技术，新模型具有更广泛的适用范围，而不是灰色模型，灰色模型具有固定的结构，有时会因过于强烈的假设而过度指定。出于实验目的，神经微分灰色模型在两个真实样本上实施，即：原油生产和喀麦隆石油产品消费。为了验证新模型，将结果与竞争模型获得的结果进行比较。看来，用于预测喀麦隆原油石油产品消耗和产量的新神经差分灰色模型的模拟和验证样本精度分别比竞争模型高 16% 和 25%。

原创性/价值

本文还深入研究了新模型的机制，从而揭示了新模型与灰色竞争模型之间的内在差异。