Differential neural network approximation of positive systems: An asymmetric barrier Lyapunov functions approach for learning laws design-Reference-Cited by-同舟云学术

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Differential neural network approximation of positive systems: An asymmetric barrier Lyapunov functions approach for learning laws design

Published:2021-10 Issue: Volume:457 Page:128-140
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Andrianova Olga,Poznyak Alexander,Chairez Isaac

Funder

Government Council on Grants, Russian Federation

Ministry of Science and Higher Education of the Russian Federation

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference28 articles.

1. Reachability, observability, and realizability of continuous-time positive systems;Ohta;SIAM J. Control Optim.,1984

2. A. Rantzer, M.E. Valcher, A tutorial on positive systems and large scale control, in: 2018 IEEE Conference on Decision and Control (CDC), IEEE, 2018, pp. 3686–3697.

3. B. Anderson, New developments in the theory of positive systems, in: Systems and Control in the Twenty-First Century, Springer, 1997, pp. 17–36.

4. Positive and compartmental systems;Benvenuti;IEEE Trans. Autom. Control,2002

5. A. Kelkar, S. Joshi, Robust control of non-passive systems via passification [for passification read passivation], in: Proceedings of the 1997 American Control Conference (Cat. No. 97CH36041), Vol. 5, IEEE, 1997, pp. 2657–2661.

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