Data-driven memory-dependent abstractions of dynamical systems via a Cantor-Kantorovich metric
<|reference_start|>Data-driven memory-dependent abstractions of dynamical systems via a Cantor-Kantorovich metric: Abstractions of dynamical systems enable their verification and the design of feedback controllers using simpler, usually discrete, models. In this paper, we propose a data-driven abstraction mechanism based on a novel metric between Markov models. Our approach is based purely on observing output labels of the underlying dynamics, thus opening the road for a fully data-driven approach to construct abstractions. Another feature of the proposed approach is the use of memory to better represent the dynamics in a given region of the state space. We show through numerical examples the usefulness of the proposed methodology.<|reference_end|>
@article{banse2024data-driven,
    title={Data-driven memory-dependent abstractions of dynamical systems via a
  Cantor-Kantorovich metric},
    author={Adrien Banse, Licio Romao, Alessandro Abate, Rapha"el M. Jungers},
    journal={arXiv preprint arXiv:2405.08353},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08353},
    primaryClass={eess.SY cs.SY}
}