Issue |
MATEC Web Conf.
Volume 336, 2021
2020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
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Article Number | 08018 | |
Number of page(s) | 7 | |
Section | Network and Information Security | |
DOI | https://doi.org/10.1051/matecconf/202133608018 | |
Published online | 15 February 2021 |
Event-triggered H∞ state estimation for time-varying neural networks with variance-constraint and fading measurements
Department of Mathematics, Harbin University of Science and Technology, Harbin 150080, China
* Corresponding author: hujun2013@gmail.com
This paper addresses the event-triggered H∞ state estimation problem for a class of discrete recurrent neural networks subject to variance-constraint and fading measurements. The phenomena of fading measurements are described by introducing a set of mutually independent random variables, which reflect that each sensor has individual missing probability. In addition, for the purpose of energy saving, an event-triggered H∞ state estimation scheme is used for time-varying neural networks to determine whether the measurement output is transmitted to the estimator or not. Some sufficient conditions are obtained to guarantee that the estimation error system satisfies both estimation error variance constraint and prescribed H∞ performance requirement. Finally, the feasibility of the proposed event-triggered H∞ state estimation method is verified by a numerical example.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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