Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online learning of neural networks based on a model-free control
  algorithm   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.

17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat