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Finite-Time Synchronization Analysis for BAM Neural Networks with Time-Varying Delays by Applying the Maximum-Value Approach with New Inequalities

Author

Listed:
  • Zhen Yang

    (Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao 266237, China)

  • Zhengqiu Zhang

    (School of Mathematics, Hunan University, Changsha 410082, China)

Abstract

In this paper, we consider the finite-time synchronization for drive-response BAM neural networks with time-varying delays. Instead of using the finite-time stability theorem and integral inequality method, by using the maximum-value method, two new criteria are obtained to ensure the finite-time synchronization for the considered drive-response systems. The inequalities in our paper, applied to obtaining the maximum-valued and designing the novel controllers, are different from those in existing papers.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:835-:d:765168
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    References listed on IDEAS

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    1. Guo, Runan & Zhang, Ziye & Liu, Xiaoping & Lin, Chong, 2017. "Existence, uniqueness, and exponential stability analysis for complex-valued memristor-based BAM neural networks with time delays," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 100-117.
    2. Wang, Weiping & Yu, Minghui & Luo, Xiong & Liu, Linlin & Yuan, Manman & Zhao, Wenbing, 2017. "Synchronization of memristive BAM neural networks with leakage delay and additive time-varying delay components via sampled-data control," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 84-97.
    3. Pratap, A. & Raja, R. & Cao, J. & Rihan, Fathalla A. & Seadawy, Aly R., 2020. "Quasi-pinning synchronization and stabilization of fractional order BAM neural networks with delays and discontinuous neuron activations," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
    4. Zhang, Weiwei & Cao, Jinde & Wu, Ranchao & Chen, Dingyuan & Alsaadi, Fuad E., 2018. "Novel results on projective synchronization of fractional-order neural networks with multiple time delays," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 76-83.
    5. Du, Feifei & Lu, Jun-Guo, 2021. "New criterion for finite-time synchronization of fractional order memristor-based neural networks with time delay," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    6. Hongyun Yan & Yuanhua Qiao & Lijuan Duan & Ling Zhang, 2020. "Global Mittag–Leffler Stabilization of Fractional-Order BAM Neural Networks with Linear State Feedback Controllers," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, August.
    7. Zhang, Jianmei & Wu, Jianwei & Bao, Haibo & Cao, Jinde, 2018. "Synchronization analysis of fractional-order three-neuron BAM neural networks with multiple time delays," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 441-450.
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    Cited by:

    1. Pengfei Guo & Yunong Zhang, 2022. "Tracking Control for Triple-Integrator and Quintuple-Integrator Systems with Single Input Using Zhang Neural Network with Time Delay Caused by Backward Finite-Divided Difference Formulas for Multiple-," Mathematics, MDPI, vol. 10(9), pages 1-27, April.
    2. 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.
    3. 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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