Fractional gradient descent has been studied extensively, with a focus on its ability to extend traditional gradient descent methods by incorporating fractional-order derivatives. This approach allows for more flexibility in navigating complex optimization landscapes and offers advantages in certain types of problems, particularly those involving non-linearities and chaotic dynamics. Yet, the challenge of fine-tuning the fractional order parameters remains unsolved. In this work, we demonstrate that it is possible to train a neural network to predict the order of the gradient effectively.

翻译：分数阶梯度下降已被广泛研究，其核心在于通过引入分数阶导数来扩展传统梯度下降方法。该方法在处理复杂优化曲面时具有更高的灵活性，并在特定类型问题中展现出优势，尤其适用于涉及非线性与混沌动力学的场景。然而，分数阶参数的精细调优问题尚未得到解决。本研究证明，可通过训练神经网络来有效预测梯度阶数。