The paper presents a method for using fractional concepts in a neural network to modify the activation and loss functions. The methodology allows the neural network to define and optimize its activation functions by determining the fractional derivative order of the training process as an additional hyperparameter. This will enable neurons in the network to adjust their activation functions to match input data better and reduce output errors, potentially improving the network's overall performance.

翻译：本文提出了一种在神经网络中应用分数阶概念来改进激活函数与损失函数的方法。该方法允许神经网络通过将训练过程中的分数阶导数阶数作为额外超参数进行定义和优化，从而自主调整激活函数。这一机制使网络中的神经元能够更匹配输入数据地调节激活函数，减少输出误差，进而可能提升网络的整体性能。