Fractional Concepts in Neural Networks: Enhancing Activation and Loss
  Functions
Zahra Alijani Vojtech Molek
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.