@inproceedings{88dab72a0c3e42ec9366d6029c23d391,
title = "Interval observer design of dynamical systems with neural networks",
abstract = "This paper proposes an interval observer design method to construct lower-bound and upper-bound of system state trajectories in run time. The developed interval observer consists of two auxiliary neural networks derived from the neural network in dynamical systems, and two observer gains to ensure the positivity and the convergence of the corresponding error dynamics. Particularly, if the neural network is driven by the output of the system, the developed approach contains a promising neural-network-free design feature. The developed method is validated with evaluations on an adaptive cruise control system with a neural network controller.",
keywords = "dynamical systems, interval observer, neural networks, runtime monitoring",
author = "Weiming Xiang",
note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 24th ACM International Conference on Hybrid Systems Computation and Control, HSCC 2021, held as part of the 14th Cyber Physical Systems and Internet-of-Things Week, CPS-IoT Week 2021 ; Conference date: 19-05-2021 Through 21-05-2021",
year = "2021",
month = may,
day = "19",
doi = "10.1145/3447928.3456662",
language = "English (US)",
series = "HSCC 2021 - Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control (part of CPS-IoT Week)",
publisher = "Association for Computing Machinery, Inc",
booktitle = "HSCC 2021 - Proceedings of the 24th International Conference on Hybrid Systems",
}