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New finite-time passivity criteria for delayed fractional-order neural networks based on Lyapunov function approach

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  • Shafiya, M.
  • Nagamani, G.

Abstract

This paper deals with the problem of finite-time passivity analysis for a class of fractional-order neural networks with constant time delay. Firstly, based on the existing passivity definition, some new concepts namely, finite-time passivity, finite-time input strict passivity, finite-time output strict passivity, and finite-time strict passivity are introduced in terms of Lyapunov function for fractional-order neural networks. In this paper, for the first time, by defining an appropriate controller and by exploiting the introduced definitions, some novel delay-dependent and order-dependent sufficient conditions ensuring the passivity performances are obtained for the addressed system. In addition, the finite-time stability conditions are also presented with an explicit formula for determining the value of setting time for stability. Finally, one numerical example is given to verify the effectiveness of the obtained theoretical results and the simulation results are provided for better understanding of the proposed problem.

Suggested Citation

  • Shafiya, M. & Nagamani, G., 2022. "New finite-time passivity criteria for delayed fractional-order neural networks based on Lyapunov function approach," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:chsofr:v:158:y:2022:i:c:s0960077922002156
    DOI: 10.1016/j.chaos.2022.112005
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    References listed on IDEAS

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    1. 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).
    2. Liang, Song & Wu, Ranchao & Chen, Liping, 2016. "Adaptive pinning synchronization in fractional-order uncertain complex dynamical networks with delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 49-62.
    3. Du, Feifei & Lu, Jun-Guo, 2021. "New approach to finite-time stability for fractional-order BAM neural networks with discrete and distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    4. Li, Hong-Li & Kao, Yonggui & Hu, Cheng & Jiang, Haijun & Jiang, Yao-Lin, 2021. "Robust exponential stability of fractional-order coupled quaternion-valued neural networks with parametric uncertainties and impulsive effects," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
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    Cited by:

    1. Stamova, Ivanka & Stamov, Trayan & Stamov, Gani, 2022. "Lipschitz stability analysis of fractional-order impulsive delayed reaction-diffusion neural network models," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    2. Li, Peiluan & Gao, Rong & Xu, Changjin & Ahmad, Shabir & Li, Ying & Akgül, Ali, 2023. "Bifurcation behavior and PDγ control mechanism of a fractional delayed genetic regulatory model," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    3. Li, Ruihong & Li, Xingxin & Gan, Qintao & Wu, Huaiqin & Cao, Jinde, 2023. "Finite time event-triggered consensus of variable-order fractional multi-agent systems," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    4. Wang, Chen & Zhang, Hai & Ye, Renyu & Zhang, Weiwei & Zhang, Hongmei, 2023. "Finite time passivity analysis for Caputo fractional BAM reaction–diffusion delayed neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 424-443.

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