The research on using robust functions for neural networks

The paper is devoted to analyzing the ability of using robust functions for building neural networks. The research highlights different robust functions in terms of applying them for obtaining a robust modification of the back-propagation algorithm. The algorithm requires that the used loss function should be infinitely or continuously differentiable. The analysis of twelve different functions has been done. The derivate of Charbonnier function has been obtained by the author. The results of analysis shows which functions can be used for the further investigation and which ones should be excluded from it.