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New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays

Author

Listed:
  • Jun Wang

    (College of Electrical and Information Engineering, Southwest Minzu University, Chengdu 610041, China)

  • Yongqiang Tian

    (Huawei Technologies Co., Ltd., Chengdu 611700, China)

  • Lanfeng Hua

    (School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Kaibo Shi

    (School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China
    Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, China
    Data Recovery Key Laboratory of Sichuan Province, College of Mathematics and Information Science, Neijiang Normal University, Neijiang 641100, China)

  • Shouming Zhong

    (School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Shiping Wen

    (Faculty of Engineering and Information Technology, Australian AI Institute, University of Technology Sydney, Ultimo, NSW 2007, Australia)

Abstract

In this work, we are concerned with the finite-time synchronization (FTS) control issue of the drive and response delayed memristor-based inertial neural networks (MINNs). Firstly, a novel finite-time stability lemma is developed, which is different from the existing finite-time stability criteria and extends the previous results. Secondly, by constructing an appropriate Lyapunov function, designing effective delay-dependent feedback controllers and combining the finite-time control theory with a new non-reduced order method (NROD), several novel theoretical criteria to ensure the FTS for the studied MINNs are provided. In addition, the obtained theoretical results are established in a more general framework than the previous works and widen the application scope. Lastly, we illustrate the practicality and validity of the theoretical results via some numerical examples.

Suggested Citation

  • Jun Wang & Yongqiang Tian & Lanfeng Hua & Kaibo Shi & Shouming Zhong & Shiping Wen, 2023. "New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays," Mathematics, MDPI, vol. 11(3), pages 1-18, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:684-:d:1050401
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    References listed on IDEAS

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    1. Wang, Qingyun & Perc, Matjaž & Duan, Zhisheng & Chen, Guanrong, 2010. "Impact of delays and rewiring on the dynamics of small-world neuronal networks with two types of coupling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3299-3306.
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    Cited by:

    1. Muhammad Maaruf & Waleed M. Hamanah & Mohammad A. Abido, 2023. "Hybrid Backstepping Control of a Quadrotor Using a Radial Basis Function Neural Network," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
    2. Wei Wang & Jinming Liang & Mihan Liu & Liming Ding & Hongbing Zeng, 2024. "Novel Robust Stability Criteria for Lur’e Systems with Time-Varying Delay," Mathematics, MDPI, vol. 12(4), pages 1-12, February.
    3. Ruixia Liu & Lei Xing & Hong Deng & Weichao Zhong, 2023. "Finite-Time Adaptive Fuzzy Control for Unmodeled Dynamical Systems with Actuator Faults," Mathematics, MDPI, vol. 11(9), pages 1-22, May.
    4. Fengjiao Zhang & Yinfang Song & Chao Wang, 2023. "α -Synchronization of a Class of Unbounded Delayed Inertial Cohen–Grossberg Neural Networks with Delayed Impulses," Mathematics, MDPI, vol. 11(19), pages 1-18, September.
    5. Zhen Yang & Zhengqiu Zhang, 2023. "New Results on Finite-Time Synchronization of Complex-Valued BAM Neural Networks with Time Delays by the Quadratic Analysis Approach," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    6. Xiong, Kailong & Hu, Cheng & Yu, Juan, 2023. "Direct approach-based synchronization of fully quaternion-valued neural networks with inertial term and time-varying delay," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    7. Juan Yu & Kailong Xiong & Cheng Hu, 2024. "Synchronization Analysis for Quaternion-Valued Delayed Neural Networks with Impulse and Inertia via a Direct Technique," Mathematics, MDPI, vol. 12(7), pages 1-22, March.
    8. Yupeng Shi & Dayong Ye, 2023. "Stability Analysis of Delayed Neural Networks via Composite-Matrix-Based Integral Inequality," Mathematics, MDPI, vol. 11(11), pages 1-13, May.

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