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Switching-Like Event-Triggered State Estimation for Reaction–Diffusion Neural Networks Against DoS Attacks

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Abstract

In this paper, event-triggered state estimation for reaction–diffusion neural networks (RDNNs) subject to Denial-of-Service (DoS) attacks is investigated. A switching-like event-triggered strategy (SETS) is proposed to handle intermittent DoS attacks, meanwhile, alleviate the burden of the network while preserving the accepted performance of the considered systems. Moreover, to obtain the unknown state, the corresponding state estimator of RDNNs is constructed. Furthermore, by virtue of a piecewise Lyapunov–Krasovskii functional method, sufficient conditions are obtained to ensure the exponential stability of the closed-loop systems. Finally, a numerical simulation is provided to demonstrate the feasibility and advantages of the obtained results.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 62203153 and 61976081, in part by the Natural Science Fund for Excellent Young Scholars of Henan Province under Grant 202300410127, in part by Key Scientific Research Projects of Higher Education Institutions in Henan Province under Grant 22A413001, in part by Top Young Talents in Central Plains under Grant Yuzutong (2021) 44, in part by Technology Innovative Teams in University of Henan Province under Grant 23IRTSTHN012, and in part by the Natural Science Fund for Young Scholars of Henan Province under Grant 222300420151, and in part by the Serbian Ministry of Education, Science and Technological Development (No. 451-03-68/2022-14/200108).

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Authors and Affiliations

  1. The School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China

    Xiaona Song, Nana Wu & Shuai Song

  2. Department of Automatic Control, Robotics and Fluid Technique, Faculty of Mechanical and Civil Engineering, University of Kragujevac, Kraljevo, 36000, Serbia

    Vladimir Stojanovic

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  1. Xiaona Song
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Correspondence to Shuai Song.

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Song, X., Wu, N., Song, S. et al. Switching-Like Event-Triggered State Estimation for Reaction–Diffusion Neural Networks Against DoS Attacks. Neural Process Lett 55, 8997–9018 (2023). https://doi.org/10.1007/s11063-023-11189-1

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