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Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation

Author

Listed:
  • Saravanan Shanmugam

    (Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, Tamilnadu, India)

  • Rajarathinam Vadivel

    (Department of Mathematics, Faculty of Science and Technology, Phuket Rajabhat University, Phuket 83000, Thailand)

  • Nallappan Gunasekaran

    (Computational Intelligence Laboratory, Toyota Technological Institute, Nagoya 468-8511, Japan)

Abstract

In this paper, we present a finite-time synchronization (FTS) for quantized Markovian-jump time-varying delayed neural networks (QMJTDNNs) via event-triggered control. The QMJTDNNs take into account the effects of quantization on the system dynamics and utilize a combination of FTS and event-triggered communication to mitigate the effects of communication delays, quantization error, and efficient synchronization. We analyze the FTS and convergence properties of the proposed method and provide simulation results to demonstrate its effectiveness in synchronizing a network of QMJTDNNs. We introduce a new method to achieve the FTS of a system that has input constraints. The method involves the development of the Lyapunov–Krasovskii functional approach (LKF), novel integral inequality techniques, and some sufficient conditions, all of which are expressed as linear matrix inequalities (LMIs). Furthermore, the study presents the design of an event-triggered controller gain for a larger sampling interval. The effectiveness of the proposed method is demonstrated through numerical examples.

Suggested Citation

  • Saravanan Shanmugam & Rajarathinam Vadivel & Nallappan Gunasekaran, 2023. "Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation," Mathematics, MDPI, vol. 11(10), pages 1-24, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2257-:d:1144816
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    References listed on IDEAS

    as
    1. Wu, Tao & Cao, Jinde & Xiong, Lianglin & Zhang, Haiyang & Shu, Jinlong, 2022. "Sampled-data synchronization criteria for Markovian jumping neural networks with additive time-varying delays using new techniques," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    2. Zhang, Dian & Cheng, Jun & Cao, Jinde & Zhang, Dan, 2019. "Finite-time synchronization control for semi-Markov jump neural networks with mode-dependent stochastic parametric uncertainties," Applied Mathematics and Computation, Elsevier, vol. 344, pages 230-242.
    3. Shuailei Zhang & Xinge Liu & Xuemei Li, 2022. "Finite-time synchronisation of delayed fractional-order coupled neural networks," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(12), pages 2597-2611, September.
    4. Zou, Cong & Li, Bing & Liu, Feiyang & Xu, Bingrui, 2022. "Event-Triggered μ-state estimation for Markovian jumping neural networks with mixed time-delays," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    5. 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).
    6. Zhang, Mengjiao & Zang, Hongyan & Bai, Luyuan, 2022. "A new predefined-time sliding mode control scheme for synchronizing chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    7. Chang, Xiao-Heng & Jin, Xue, 2022. "Observer-based fuzzy feedback control for nonlinear systems subject to transmission signal quantization," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    8. Song, Xingxing & Lu, Hongqian & Xu, Yao & Zhou, Wuneng, 2022. "H∞ synchronization of semi-Markovian jump neural networks with random sensor nonlinearities via adaptive event-triggered output feedback control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 198(C), pages 1-19.
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