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Exponential synchronization of memristive neural networks with inertial and nonlinear coupling terms: Pinning impulsive control approaches

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  • Fu, Qianhua
  • Zhong, Shouming
  • Shi, Kaibo

Abstract

This paper investigates exponential synchronization for memristive neural networks (MNNs) with inertial and nonlinear coupling terms. Two novel hybrid mode-dependent pinning impulse control approaches are proposed, one is adaptive element-selection and pinning a part of elements in each identical node, and the other is fixed node-selection and pinning a part of identical nodes. By introducing appropriate variable substitution, the initial second-order state derivative model for MNNs is transformed into two first-order derivative equations. Then, through introducing average impulsive interval, Lyapunov–Krasovskii functional method, inequality techniques, and extended comparison principle, some corresponding exponential synchronization conditions are presented, which enrich and extend some published results. Finally, simulations are given to illustrate the exponential synchronization conditions.

Suggested Citation

  • Fu, Qianhua & Zhong, Shouming & Shi, Kaibo, 2021. "Exponential synchronization of memristive neural networks with inertial and nonlinear coupling terms: Pinning impulsive control approaches," Applied Mathematics and Computation, Elsevier, vol. 402(C).
    • Handle: RePEc:eee:apmaco:v:402:y:2021:i:c:s0096300321002599
    DOI: 10.1016/j.amc.2021.126169
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    References listed on IDEAS

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    1. Jian Liu & Rui Xu, 2018. "Adaptive synchronisation of memristor-based neural networks with leakage delays and applications in chaotic masking secure communication," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(6), pages 1300-1315, April.
    2. Yi, Chengbo & Feng, Jianwen & Wang, Jingyi & Xu, Chen & Zhao, Yi, 2017. "Synchronization of delayed neural networks with hybrid coupling via partial mixed pinning impulsive control," Applied Mathematics and Computation, Elsevier, vol. 312(C), pages 78-90.
    3. Chen, Siya & Feng, Jianwen & Wang, Jingyi & Zhao, Yi, 2020. "Almost sure exponential synchronization of drive-response stochastic memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 383(C).
    4. Chen, Chuan & Li, Lixiang & Peng, Haipeng & Yang, Yixian & Mi, Ling & Qiu, Baolin, 2019. "Fixed-time projective synchronization of memristive neural networks with discrete delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    5. Wu, Tianyu & Huang, Xia & Chen, Xiangyong & Wang, Jing, 2020. "Sampled-data H∞ exponential synchronization for delayed semi-Markov jump CDNs: A looped-functional approach," Applied Mathematics and Computation, Elsevier, vol. 377(C).
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    Cited by:

    1. Kowsalya, P. & Mohanrasu, S.S. & Kashkynbayev, Ardak & Gokul, P. & Rakkiyappan, R., 2024. "Fixed-time synchronization of Inertial Cohen-Grossberg Neural Networks with state dependent delayed impulse control and its application to multi-image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    2. Xiu, Chunbo & Fang, Jingyao & Liu, Yuxia, 2022. "Design and circuit implementation of a novel 5D memristive CNN hyperchaotic system," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).

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