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

## TL;DR

This paper proposes a novel model-free control law for online training of neural networks, framing weight tuning as a feedback control problem, demonstrated through promising numerical results and classification examples.

## Contribution

It introduces a new model-free control approach for online neural network training, offering an alternative to traditional methods.

## Key findings

- Effective online weight adjustment demonstrated
- Numerical results show promising learning dynamics
- Classifier example confirms approach viability

## 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.