  Recently, the term explainable AI became known as an approach to produce
models from artificial intelligence which allow interpretation. Since a long
time, there are models of symbolic regression in use that are perfectly
explainable and mathematically tractable: in this contribution we demonstrate
how to use symbolic regression methods to infer the optimal control of a
dynamical system given one or several optimization criteria, or cost functions.
In previous publications, network control was achieved by automatized machine
learning control using genetic programming. Here, we focus on the subsequent
analysis of the analytical expressions which result from the machine learning.
In particular, we use AUTO to analyze the stability properties of the
controlled oscillator system which served as our model. As a result, we show
that there is a considerable advantage of explainable models over less
accessible neural networks.
