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Bipartite synchronization of coupled delayed neural networks with cooperative-competitive interaction via event-triggered control

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  • Zhu, Sha
  • Bao, Haibo
  • Cao, Jinde

Abstract

In this paper, the issue of bipartite synchronization for coupled delayed neural networks (CDNNs) with cooperative-competitive interaction is firstly investigated via event-triggered control (ETC) mechanism, which greatly reduces communication bandwidth and computational burden. Firstly, in virtue of the signed graph theory, the model of CDNNs with cooperative-competitive interaction is established. Next, under the designed event-triggered controller, statical event-triggered control (SETC) condition and dynamical event-triggered control (DETC) condition for realizing bipartite synchronization of CDNNs are derived separately by utilizing Lyapunov functional method. Furthermore, an algebraic formula is provided to calculate the positive lower bound of the trigger interval, which indicates that Zeno-behavior does not arise. At last, the feasibility of the proffered ETC to realize bipartite synchronization is further demonstrated by an illustrative example.

Suggested Citation

  • Zhu, Sha & Bao, Haibo & Cao, Jinde, 2022. "Bipartite synchronization of coupled delayed neural networks with cooperative-competitive interaction via event-triggered control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
  • Handle: RePEc:eee:phsmap:v:600:y:2022:i:c:s0378437122004046
    DOI: 10.1016/j.physa.2022.127586
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    1. Bernardo A. Huberman & Lada A. Adamic, 1999. "Growth dynamics of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 131-131, September.
    2. Cui-Qin Ma & Wei-Guo Sun, 2018. "Bipartite Consensus for Multiagent Systems via Event-Based Control," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-8, October.
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    Cited by:

    1. Gao, Panqing & Ye, Renyu & Zhang, Hai & Stamova, Ivanka & Cao, Jinde, 2024. "Asymptotic stability and quantitative synchronization of fractional competitive neural networks with multiple restrictions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 338-353.

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