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Finite-Time Passivity Analysis of Neutral-Type Neural Networks with Mixed Time-Varying Delays

Author

Listed:
  • Issaraporn Khonchaiyaphum

    (Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Nayika Samorn

    (Faculty of Agriculture and Technology, Nakhon Phanom University, Nakhon Phanom 48000, Thailand)

  • Thongchai Botmart

    (Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Kanit Mukdasai

    (Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand)

Abstract

This research study investigates the issue of finite-time passivity analysis of neutral-type neural networks with mixed time-varying delays. The time-varying delays are distributed, discrete and neutral in that the upper bounds for the delays are available. We are investigating the creation of sufficient conditions for finite boundness, finite-time stability and finite-time passivity, which has never been performed before. First, we create a new Lyapunov–Krasovskii functional, Peng–Park’s integral inequality, descriptor model transformation and zero equation use, and then we use Wirtinger’s integral inequality technique. New finite-time stability necessary conditions are constructed in terms of linear matrix inequalities in order to guarantee finite-time stability for the system. Finally, numerical examples are presented to demonstrate the result’s effectiveness. Moreover, our proposed criteria are less conservative than prior studies in terms of larger time-delay bounds.

Suggested Citation

  • Issaraporn Khonchaiyaphum & Nayika Samorn & Thongchai Botmart & Kanit Mukdasai, 2021. "Finite-Time Passivity Analysis of Neutral-Type Neural Networks with Mixed Time-Varying Delays," Mathematics, MDPI, vol. 9(24), pages 1-26, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:24:p:3321-:d:706540
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    References listed on IDEAS

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    1. Li, Jiarong & Jiang, Haijun & Hu, Cheng & Yu, Juan, 2018. "Analysis and discontinuous control for finite-time synchronization of delayed complex dynamical networks," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 291-305.
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    Cited by:

    1. Quanxin Zhu, 2022. "Nonlinear Systems: Dynamics, Control, Optimization and Applications to the Science and Engineering," Mathematics, MDPI, vol. 10(24), pages 1-2, December.
    2. Bin Li & Jiahao Zhu & Ranran Zhou & Guoxing Wen, 2022. "Adaptive Neural Network Sliding Mode Control for a Class of SISO Nonlinear Systems," Mathematics, MDPI, vol. 10(7), pages 1-12, April.

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