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Event-triggered extended dissipative synchronization for delayed neural networks with random uncertainties

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  • Karnan, A.
  • Nagamani, G.

Abstract

This paper aims to study a generalized robust dynamical behavior called the extended dissipative synchronization of neural networks with time-varying delay and random uncertainties. To achieve the primary objective of minimizing network resource utilization while preserving desired closed-loop performance, an event-triggered control scheme is implemented. By constructing an augmented form of Lyapunov–Krasovskii functional and utilizing generalized integral inequalities, two novel synchronization criteria have been proposed in the form of linear matrix inequalities. It is worth noting that this paper studies a generalized dissipative performance index, enabling the various event-based synchronization problems, including H∞, L2−L∞, passivity, and (Q,S,R)−γ−dissipative synchronization in a unified framework. Ultimately, the efficacy and benefits of the suggested approach are demonstrated through two numerical examples.

Suggested Citation

  • Karnan, A. & Nagamani, G., 2023. "Event-triggered extended dissipative synchronization for delayed neural networks with random uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    • Handle: RePEc:eee:chsofr:v:175:y:2023:i:p1:s0960077923008834
    DOI: 10.1016/j.chaos.2023.113982
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    References listed on IDEAS

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    1. Zhao, Yong & Ren, Shanshan & Kurths, Jürgen, 2021. "Finite-time and fixed-time synchronization for a class of memristor-based competitive neural networks with different time scales," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    2. Jun Zhou & Dongbing Tong & Qiaoyu Chen & Wuneng Zhou, 2020. "Master-slave synchronization of neural networks with time-varying delays via the event-triggered control," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 26(4), pages 357-373, July.
    3. Duan, Lian & Liu, Jinzhi & Huang, Chuangxia & Wang, Zengyun, 2022. "Finite-/fixed-time anti-synchronization of neural networks with leakage delays under discontinuous disturbances," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    4. Gao, Zifan & Zhang, Dawei & Zhu, Shuqian, 2023. "Hybrid event-triggered synchronization control of delayed chaotic neural networks against communication delay and random data loss," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    5. Wang, Weiping & Sun, Yue & Yuan, Manman & Wang, Zhen & Cheng, Jun & Fan, Denggui & Kurths, Jürgen & Luo, Xiong & Wang, Chunyang, 2021. "Projective synchronization of memristive multidirectional associative memory neural networks via self-triggered impulsive control and its application to image protection," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    6. Huan, Mingchen & Li, Chuandong, 2023. "Synchronization of reaction–diffusion neural networks with sampled-data control via a new two-sided looped-functional," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    7. Vadivel, R. & Hammachukiattikul, P. & Gunasekaran, Nallappan & Saravanakumar, R. & Dutta, Hemen, 2021. "Strict dissipativity synchronization for delayed static neural networks: An event-triggered scheme," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
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    Cited by:

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