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Synchronization of Takagi–Sugeno Fuzzy Time-Delayed Stochastic Bidirectional Associative Memory Neural Networks Driven by Brownian Motion in Pre-Assigned Settling Time

Author

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  • Chengqiang Wang

    (School of Mathematics, Suqian University, Suqian 223800, China
    School of Mathematical and Computational Science, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Xiangqing Zhao

    (School of Mathematics, Suqian University, Suqian 223800, China)

  • Can Wang

    (School of Mathematics, Chengdu Normal University, Chengdu 611130, China)

  • Zhiwei Lv

    (School of Mathematics, Suqian University, Suqian 223800, China)

Abstract

We are devoted, in this paper, to the study of the pre-assigned-time drive-response synchronization problem for a class of Takagi–Sugeno fuzzy logic-based stochastic bidirectional associative memory neural networks, driven by Brownian motion, with continuous-time delay and (finitely and infinitely) distributed time delay. To achieve the drive-response synchronization between the neural network systems, concerned in this paper, and the corresponding response neural network systems (identical to our concerned neural network systems), we bring forward, based on the structural properties, a class of control strategies. By meticulously coining an elaborate Lyapunov–Krasovskii functional, we prove a criterion guaranteeing the desired pre-assigned-time drive-response synchronizability: For any given positive time instant, some of our designed controls make sure that our concerned neural network systems and the corresponding response neural network systems achieve synchronization, with the settling times not exceeding the pre-assigned positive time instant. In addition, we equip our theoretical studies with a numerical example, to illustrate that the synchronization controls designed in this paper are indeed effective. Our concerned neural network systems incorporate several types of time delays simultaneously, in particular, they have a continuous-time delay in their leakage terms, are based on Takagi–Sugeno fuzzy logic, and can be synchronized before any pre-given finite-time instant by the suggested control; therefore, our theoretical results in this paper have wide potential applications in the real world. The conservatism is reduced by introducing parameters in our designed Lyapunov–Krasovskii functional and synchronization control.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3697-:d:1226987
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    References listed on IDEAS

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    1. Chen, Dazhao & Zhang, Zhengqiu, 2022. "Globally asymptotic synchronization for complex-valued BAM neural networks by the differential inequality way," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    2. Wang, Shuzhan & Zhang, Ziye & Lin, Chong & Chen, Jian, 2021. "Fixed-time synchronization for complex-valued BAM neural networks with time-varying delays via pinning control and adaptive pinning control," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    3. 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).
    4. Chen, Dazhao & Zhang, Zhengqiu, 2022. "Finite-time synchronization for delayed BAM neural networks by the approach of the same structural functions," Chaos, Solitons & Fractals, Elsevier, vol. 164(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.
    6. 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.
    7. Sriraman, R. & Cao, Yang & Samidurai, R., 2020. "Global asymptotic stability of stochastic complex-valued neural networks with probabilistic time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 103-118.
    8. 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.
    9. Ratnavelu, K. & Manikandan, M. & Balasubramaniam, P., 2015. "Synchronization of fuzzy bidirectional associative memory neural networks with various time delays," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 582-605.
    10. Ruofeng Rao & Zhi Lin & Xiaoquan Ai & Jiarui Wu, 2022. "Synchronization of Epidemic Systems with Neumann Boundary Value under Delayed Impulse," Mathematics, MDPI, vol. 10(12), pages 1-10, June.
    11. Zhen Yang & Zhengqiu Zhang, 2022. "Finite-Time Synchronization Analysis for BAM Neural Networks with Time-Varying Delays by Applying the Maximum-Value Approach with New Inequalities," Mathematics, MDPI, vol. 10(5), pages 1-16, March.
    12. Wang, Tianyu & Zhu, Quanxin, 2019. "Stability analysis of stochastic BAM neural networks with reaction–diffusion, multi-proportional and distributed delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    13. 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.
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