Exponential synchronization of coupled neural networks under stochastic deception attacks

In this paper, the issue of synchronization is investigated for coupled neural networks subject to stochastic deception attacks. Firstly, a general differential inequality with delayed impulses is given. Then, the established differential inequality is further extended to the case of delayed stochastic impulses, in which both the impulsive instants and impulsive intensity are stochastic. Secondly, by modeling the stochastic discrete-time deception attacks as stochastic impulses, synchronization criteria of the coupled neural networks under the corresponding attacks are given. Finally, two numerical examples are provided to demonstrate the correctness of the theoretical results. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates the synchronization issue for coupled neural networks under stochastic deception attacks, providing synchronization criteria and extending the established differential inequality to delayed stochastic impulses. By modeling stochastic discrete-time deception attacks as stochastic impulses, the paper offers theoretical results validated through numerical examples.