  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.
