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Fixed-time synchronization of Inertial Cohen-Grossberg Neural Networks with state dependent delayed impulse control and its application to multi-image encryption

Author

Listed:
  • Kowsalya, P.
  • Mohanrasu, S.S.
  • Kashkynbayev, Ardak
  • Gokul, P.
  • Rakkiyappan, R.

Abstract

In this paper, we discussed about fixed-time synchronization (FXTS) of Inertial Cohen-Grossberg Neural Networks (ICGNNs) with state-dependent delayed impulses. The Lyapunov stability theory and several useful criteria are utilized to make sure that the control parameters are selected in sync with the intended settling time. Two types of the controller are developed in order to guarantee that error-delayed ICGNNs can be synchronized. A sufficient condition for ensuring FXTS for delayed ICGNNs with desynchronization impulses is investigated. In FXTS, the settling time of ICGNNs will have the smallest upper bound and the settling time of desynchronization will have the largest upper bound. We subsequently conducted numerical simulations to substantiate the validity of the proposed discoveries. Finally, we proposed a multi-image encryption algorithm with the help of ICGNNs and presented the statistical analysis to test its efficacy.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:181:y:2024:i:c:s0960077924002455
    DOI: 10.1016/j.chaos.2024.114693
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    References listed on IDEAS

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    1. Han, Siyu & Hu, Cheng & Yu, Juan & Jiang, Haijun & Wen, Shiping, 2021. "Stabilization of inertial Cohen-Grossberg neural networks with generalized delays: A direct analysis approach," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    2. Long, Changqing & Zhang, Guodong & Hu, Junhao, 2021. "Fixed-time synchronization for delayed inertial complex-valued neural networks," Applied Mathematics and Computation, Elsevier, vol. 405(C).
    3. Jia-Bao Liu & Zahid Raza & Muhammad Javaid, 2020. "Zagreb Connection Numbers for Cellular Neural Networks," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-8, October.
    4. 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).
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