Adaptive dynamic event-triggered cluster synchronization in an array of coupled neural networks subject to cyber-attacks

Hongjie Li, Jinde Cao, Ardak Kashkynbayev, Shuiming Cai

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

This paper is concerned with cluster synchronization in an array of coupled neural networks subject to cyber-attacks, where a random variable is employed to reflect the success ration of the launched cyber-attacks. To save the energy consumption and alleviate the transmission load of the network, a novel adaptive distributed dynamic event-triggered communication scheme is presented based on the local stochastic the state sampling information, compared with some existing results, the different event-triggered matrices and auxiliary variables are introduced for each node to adjust its threshold dynamically, which can further reduce the sampled-data transmission, the proposed event-triggered scheme includes some existing event-triggered schemes as special cases. The cluster synchronization controllers are designed based on the states of neighboring nodes at event-triggered instants, a new stochastic sampled-data dependent error model is constructed, some mean-square cluster synchronization criteria can be derived by the Lyapunov stability theory and algebraic graph theory, and the feedback matrices and event-triggered matrices can be obtained by solving some linear matrix inequalities. Finally, two numerical examples are employed to show the validity and advantage of the theoretical results.

Original languageEnglish
Pages (from-to)380-398
Number of pages19
JournalNeurocomputing
Volume511
DOIs
Publication statusPublished - Oct 28 2022

Keywords

  • Coupled neural networks
  • Cyber-attacks
  • Dynamical event-triggered scheme
  • Mean-square cluster synchronization
  • Stochastic sampling

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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