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New results of quasi-projective synchronization for fractional-order complex-valued neural networks with leakage and discrete delays

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  • Yan, Hongyun
  • Qiao, Yuanhua
  • Duan, Lijuan
  • Miao, Jun

Abstract

In this paper, the non-decomposition method is employed to investigate the quasi-projective synchronization of fractional-order complex-valued neural networks (FOCVNNs) with leakage and discrete delays. Firstly, two new inequalities are established in complex domain, which provides a powerful tool to explore the synchronization and stability of complex-valued systems. Secondly, by means of the Banach fixed point theorem, the existence and uniqueness of solution of the delayed FOCVNNs is discussed under certain conditions. Thirdly, a linear complex-valued controller is designed to induce quasi-projective synchronization of the delayed FOCVNNs, and some novel results are given by using the presented inequalities, the non-decomposition method and the Lyapunov stability theory. Further, the error bounds are estimated. It is found that a smaller error bound can be obtained by appropriately increasing the feedback gains. Finally, two numerical examples are given to verify the effectiveness of the theoretical results and the practicability of the synchronization strategy in secure communication.

Suggested Citation

  • Yan, Hongyun & Qiao, Yuanhua & Duan, Lijuan & Miao, Jun, 2022. "New results of quasi-projective synchronization for fractional-order complex-valued neural networks with leakage and discrete delays," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    • Handle: RePEc:eee:chsofr:v:159:y:2022:i:c:s0960077922003319
    DOI: 10.1016/j.chaos.2022.112121
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    References listed on IDEAS

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    1. Wang, Fen & Chen, Yuanlong, 2021. "Mean square exponential stability for stochastic memristor-based neural networks with leakage delay," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    2. Xu, Changjin & Liao, Maoxin & Li, Peiluan & Yuan, Shuai, 2021. "Impact of leakage delay on bifurcation in fractional-order complex-valued neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    3. Wang, Fei & Zheng, Zhaowen, 2019. "Quasi-projective synchronization of fractional order chaotic systems under input saturation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    4. 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).
    5. Xu, Yao & Yu, Jintong & Li, Wenxue & Feng, Jiqiang, 2021. "Global asymptotic stability of fractional-order competitive neural networks with multiple time-varying-delay links," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    6. D. Baleanu & S. J. Sadati & R. Ghaderi & A. Ranjbar & T. Abdeljawad (Maraaba) & F. Jarad, 2010. "Razumikhin Stability Theorem for Fractional Systems with Delay," Abstract and Applied Analysis, Hindawi, vol. 2010, pages 1-9, June.
    7. Miaadi, Foued & Li, Xiaodi, 2021. "Impulsive effect on fixed-time control for distributed delay uncertain static neural networks with leakage delay," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    8. Meng Hui & Chen Wei & Jiao Zhang & Herbert Ho-Ching Iu & Ni Luo & Rui Yao & Lin Bai, 2020. "Finite-Time Projective Synchronization of Fractional-Order Memristive Neural Networks with Mixed Time-Varying Delays," Complexity, Hindawi, vol. 2020, pages 1-27, June.
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    Cited by:

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    2. Kumar, Ankit & Das, Subir & Singh, Sunny & Rajeev,, 2023. "Quasi-projective synchronization of inertial complex-valued recurrent neural networks with mixed time-varying delay and mismatched parameters," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    3. Shi, Lingna & Li, Jiarong & Jiang, Haijun & Wang, Jinling, 2023. "Quasi-synchronization of multi-layer delayed neural networks with parameter mismatches via impulsive control," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    4. Li, Xuemei & Liu, Xinge & Wang, Fengxian, 2023. "Anti-synchronization of fractional-order complex-valued neural networks with a leakage delay and time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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