TY  - JOUR
A1  - Quade, Markus
A1  - Isele, Thomas
A1  - Abel, Markus
T1  - Machine learning control
BT  - explainable and analyzable methods
JF  - Physica : D, Nonlinear phenomena
N2  - Recently, the term explainable AI came into discussion as an approach to produce models from artificial intelligence which allow interpretation. For a long time, symbolic regression has been used to produce explainable and mathematically tractable models. In this contribution, we extend previous work on symbolic regression methods to infer the optimal control of a dynamical system given one or several optimization criteria, or cost functions. In earlier publications, network control was achieved by automated machine learning control using genetic programming. Here, we focus on the subsequent path continuation analysis of the mathematical expressions which result from the machine learning model. In particular, we use AUTO to analyze the solution properties of the controlled oscillator system which served as our model. As a result, we show that there is a considerable advantage of explainable symbolic regression models over less accessible neural networks. In particular, the roadmap of future works may be to integrate such analyses into the optimization loop itself to filter out robust solutions by construction.
KW  - Explainable AI
KW  - Machine learning control
KW  - Dynamical systems
KW  - Synchronization control
KW  - Genetic programming
Y1  - 2020
U6  - https://doi.org/10.1016/j.physd.2020.132582
SN  - 0167-2789
SN  - 1872-8022
VL  - 412
PB  - Elsevier
CY  - Amsterdam
ER  -