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Predefined-Time (PDT) Synchronization of Impulsive Fuzzy BAM Neural Networks with Stochastic Perturbations

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  • Rouzimaimaiti Mahemuti

    (School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou 511363, China
    These authors contributed equally to this work.)

  • Abdujelil Abdurahman

    (Guangzhou Huayi Electronic Technology Co., Ltd., Guangzhou 511400, China
    These authors contributed equally to this work.)

Abstract

This paper focuses on the predefined-time (PDT) synchronization issue of impulsive fuzzy bidirectional associative memory neural networks with stochastic perturbations. Firstly, useful definitions and lemmas are introduced to define the PDT synchronization of the considered system. Next, a novel controller with a discontinuous sign function is designed to ensure the synchronization error converges to zero in the preassigned time. However, the sign function may cause the chattering effect, leading to undesirable results such as the performance degradation of synchronization. Hence, we designed a second novel controller to eliminate this chattering effect. After that, we obtained some sufficient conditions to guarantee the PDT synchronization of the drive–response systems by using the Lyapunov function method. Finally, three numerical simulations are provided to evaluate the validity of the theoretical results.

Suggested Citation

  • Rouzimaimaiti Mahemuti & Abdujelil Abdurahman, 2023. "Predefined-Time (PDT) Synchronization of Impulsive Fuzzy BAM Neural Networks with Stochastic Perturbations," Mathematics, MDPI, vol. 11(6), pages 1-18, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1291-:d:1090459
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    References listed on IDEAS

    as
    1. Hu, Dandan & Tan, Jieqing & Shi, Kaibo & Ding, Kui, 2022. "Switching synchronization of reaction-diffusion neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Sader, Malika & Abdurahman, Abdujelil & Jiang, Haijun, 2018. "General decay synchronization of delayed BAM neural networks via nonlinear feedback control," Applied Mathematics and Computation, Elsevier, vol. 337(C), pages 302-314.
    3. Mathiyalagan, K. & Park, Ju H. & Sakthivel, R., 2015. "Synchronization for delayed memristive BAM neural networks using impulsive control with random nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 259(C), pages 967-979.
    4. Abdurahman, Abdujelil & Abudusaimaiti, Mairemunisa & Jiang, Haijun, 2023. "Fixed/predefined-time lag synchronization of complex-valued BAM neural networks with stochastic perturbations," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    5. Jingjing You & Abdujelil Abdurahman & Hayrengul Sadik, 2022. "Fixed/Predefined-Time Synchronization of Complex-Valued Stochastic BAM Neural Networks with Stabilizing and Destabilizing Impulse," Mathematics, MDPI, vol. 10(22), pages 1-20, November.
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    Cited by:

    1. Chengqiang Wang & Xiangqing Zhao & Can Wang & Zhiwei Lv, 2023. "Synchronization of Takagi–Sugeno Fuzzy Time-Delayed Stochastic Bidirectional Associative Memory Neural Networks Driven by Brownian Motion in Pre-Assigned Settling Time," Mathematics, MDPI, vol. 11(17), pages 1-32, August.

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