Online learning of neural networks based on a model-free control algorithm

Abstract

We explore the possibilities of using a model-free-based control law in order to train artificial neural networks. In the supervised learning context, we consider the problem of tuning the synaptic weights as a feedback control tracking problem where the control algorithm adjusts the weights online according to the input-output training data set of the neural network. Numerical results illustrate the dynamical learning process and an example of classifier that show very promising properties of our proposed approach.

8 pages, 4 figures, Accepted to the APMS 2021 conference