Research article

Exponential synchronization of neural networks with mixed delays under impulsive control

  • Received: 14 July 2024 Revised: 02 September 2024 Accepted: 05 September 2024 Published: 13 September 2024
  • In this paper, the exponential synchronization problem of a class of neural networks with mixed delays under impulsive control is studied. Combining the impulsive comparison principle and the concept of an average impulsive interval, two impulsive differential inequalities with mixed delays are discussed, and the sufficient conditions for the existence of exponential decay are obtained. Based on two different impulsive control strategies, and then by means of the Lyapunov function, the inequality technique, and these two new inequalities, a set of sufficient conditions are derived to ensure the synchronization of the drive and response systems. In order to prove the effectiveness of the proposed control scheme, two numerical examples are given to prove its practicability and effectiveness.

    Citation: Wanshun Zhao, Kelin Li, Yanchao Shi. Exponential synchronization of neural networks with mixed delays under impulsive control[J]. Electronic Research Archive, 2024, 32(9): 5287-5305. doi: 10.3934/era.2024244

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  • In this paper, the exponential synchronization problem of a class of neural networks with mixed delays under impulsive control is studied. Combining the impulsive comparison principle and the concept of an average impulsive interval, two impulsive differential inequalities with mixed delays are discussed, and the sufficient conditions for the existence of exponential decay are obtained. Based on two different impulsive control strategies, and then by means of the Lyapunov function, the inequality technique, and these two new inequalities, a set of sufficient conditions are derived to ensure the synchronization of the drive and response systems. In order to prove the effectiveness of the proposed control scheme, two numerical examples are given to prove its practicability and effectiveness.



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    [1] W. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biol., 52 (1990), 99–115. https://doi.org/10.1016/S0092-8240(05)80006-0 doi: 10.1016/S0092-8240(05)80006-0
    [2] J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proc. Natl. Acad. Sci. U.S.A., 79 (1982), 2554–2558. https://doi.org/10.1073/pnas.79.8.2554 doi: 10.1073/pnas.79.8.2554
    [3] C. M. Marcus, R. M. Westervelt, Stability of analog neural networks with delay, Phys. Rev. A, 39 (1989), 347–359. https://doi.org/10.1103/PhysRevA.39.347 doi: 10.1103/PhysRevA.39.347
    [4] L.O. Chua, L. Yang, Cellular neural network: applications, EEE Trans Circuits Syst., 35 (1988), 1273–1290. https://doi.org/110.1109/31.7601
    [5] N. Takahshi, A new sufficient condition for complete stability of cellular neural networks with delays, IEEE Transactions on Circuits & Systems I Fundamental Theory & Applications, 47.6 (2000), 793–799. https://doi.org/10.1109/81.852931 doi: 10.1109/81.852931
    [6] N. Takahshi, L.O. Chua, On the complete stability of non-symmetric cellular neural networks, IEEE Transactions on Circuits & Systems I Fundamental Theory & Applications, 145 (1998), 754–758. https://doi.org/10.1109/81.703843 doi: 10.1109/81.703843
    [7] Q. Song, Exponential stability of recurrent neural networks with both time-varying delays and general activation functions via LMI approach, Neurocomputing, 71 (2008), 2823–2830. https://doi.org/10.1016/j.neucom.2007.08.024 doi: 10.1016/j.neucom.2007.08.024
    [8] S. Arik, Stability analysis of delayed neural networks, IEEE Trans. Circuits Syst. I: Fundam. Theory Appl., 47 (2000), 1089–1092. https://doi.org/10.1109/81.855465 doi: 10.1109/81.855465
    [9] X. You, S. Dian, R. Guo, S. Li, Exponential stability analysis for discrete-time quaternion-valued neural networks with leakage delay and discrete time-varying delays, Neurocomputing, 430 (2021), 71–81. https://doi.org/10.1016/j.neucom.2020.12.021 doi: 10.1016/j.neucom.2020.12.021
    [10] A. Pratap, R. Raja, J. Cao, G. Rajchakit, E. A. Fuad, Further synchronization in finite time analysis for time-varying delayed fractional order memristive competitive neural networks with leakage delay, Neurocomputing, 317 (2018), 110–126. https://doi.org/10.1016/j.neucom.2018.08.016 doi: 10.1016/j.neucom.2018.08.016
    [11] F. Miaadi, X. Li, Impulsive effect on fixed-time control for distributed delay uncertain static neural networks with leakage delay, Chaos, Solitons Fractals, 142 (2021), 110389. https://doi.org/10.1016/j.chaos.2020.110389 doi: 10.1016/j.chaos.2020.110389
    [12] K. P. Lakshmi, T. Senthilkumar, Robust exponential synchronization results for uncertain infinite time varying distributed delayed neural networks with flexible delayed impulsive control, Math. Comput. Simul., 209 (2023), 267–281. https://doi.org/10.1016/j.matcom.2023.02.009 doi: 10.1016/j.matcom.2023.02.009
    [13] S. Haykin, Neural networks and learning machines, in Pearson Education India, 2009.
    [14] S. Niculescu, Delay Effects on Stability: A Robust Control Approach, Springer-Verlag, New York, 2001. https://doi.org/10.1007/1-84628-553-4
    [15] X. Yang, Z. Yang, X. Nie, Exponential synchronization of discontinuous chaotic systems via delayed impulsive control and its application to secure communication, Commun. Nonlinear Sci. Numer. Simul., 19 (2014), 1529–1543. https://doi.org/10.1016/j.cnsns.2013.09.012 doi: 10.1016/j.cnsns.2013.09.012
    [16] Y. Wang, W. Cheng, J. Feng, S. Zang, H. Cheng, Z. Peng, et al., Silicon photonic secure communication using artificial neural network, Chaos, Solitons Fractals, 163 (2022), 112524. https://doi.org/10.1016/j.chaos.2022.112524 doi: 10.1016/j.chaos.2022.112524
    [17] O. Deqiang, S. Jie, J. Haijun, N. Sing Kiong, S. Heng Tao, Impulsive synchronization of coupled delayed neural networks with actuator saturation and its application to image encryption, Neural Networks, 128 (2020), 158–171. https://doi.org/10.1016/j.neunet.2020.05.016 doi: 10.1016/j.neunet.2020.05.016
    [18] J. Liu, L. Shu, Q. Chen, S. Zhong, Fixed-time synchronization criteria of fuzzy inertial neural networks via Lyapunov functions with indefinite derivatives and its application to image encryption, Fuzzy Sets Syst., 459 (2023), 22–42. https://doi.org/10.1016/j.fss.2022.08.002 doi: 10.1016/j.fss.2022.08.002
    [19] W. Wang, Y. Sun, M. Yuan, Z. Wang, J. Cheng, D. Fan, et al., Projective synchronization of memristive multidirectional associative memory neural networks via self-triggered impulsive control and its application to image protection, Chaos Solitons Fractals, 150 (2021), 111110. https://doi.org/10.1016/j.chaos.2021.111110 doi: 10.1016/j.chaos.2021.111110
    [20] W. Yao, C. Wang, Y. Sun, S. Gong, H. Lin, Event-triggered control for robust exponential synchronization of inertial memristive neural networks under parameter disturbance, Neural Networks, 164 (2023), 67–80. https://doi.org/10.1016/j.neunet.2023.04.024 doi: 10.1016/j.neunet.2023.04.024
    [21] Z. Jiang, F. Huang, H. Shao, S. Cai, X. Lu, S. Jiang, Time-varying finite-time synchronization analysis of attack-induced uncertain neural networks, Chaos, Solitons Fractals, 175 (2023), 113954. https://doi.org/10.1016/j.chaos.2023.113954 doi: 10.1016/j.chaos.2023.113954
    [22] S. Zhao, K. Li, W. Hu, Y. Wang, Finite-time synchronization of discontinuous fuzzy neural networks with mixed time-varying delays and impulsive disturbances, Results Control Optim., 12 (2023), 100253. https://doi.org/10.1016/j.rico.2023.100253 doi: 10.1016/j.rico.2023.100253
    [23] J. Xiao, Y. Hu, Z. Zeng, A. Wu, S. Wen, Fixed/predefined-time synchronization of memristive neural networks based on state variable index coefficient, Neurocomputing, 560 (2023), 126849. https://doi.org/10.1016/j.neucom.2023.126849 doi: 10.1016/j.neucom.2023.126849
    [24] W. Mao, S. You, Y. Jiang, X. Mao, Stochastic stabilization of hybrid neural networks by periodically intermittent control based on discrete-time state observations, Nonlinear Anal. Hybrid Syst, 48 (2023), 101331. https://doi.org/10.1016/j.nahs.2023.101331 doi: 10.1016/j.nahs.2023.101331
    [25] W. Zhou, Y. Hu, X. Liu, J. Cao, Finite-time adaptive synchronization of coupled uncertain neural networks via intermittent control, Physica A, 596 (2022), 127107. https://doi.org/10.1016/j.physa.2022.127107 doi: 10.1016/j.physa.2022.127107
    [26] J. Chen, B. Chen, Z. Zeng, Exponential quasi-synchronization of coupled delayed memristive neural networks via intermittent event-triggered control, Neural Networks, 141 (2021), 98–106. https://doi.org/10.1016/j.neunet.2021.01.013 doi: 10.1016/j.neunet.2021.01.013
    [27] J. Cai, J. Feng, J. Wang, Y. Zhao, Quasi-synchronization of neural networks with diffusion effects via intermittent control of regional division, Neurocomputing, 409 (2020), 146–156. https://doi.org/10.1016/j.neucom.2020.05.037 doi: 10.1016/j.neucom.2020.05.037
    [28] Y. Yang, Y. He, M. Wu, Intermittent control strategy for synchronization of fractional-order neural networks via piecewise Lyapunov function method, J. Franklin Inst., 356 (2019), 4648–4676. https://doi.org/10.1016/j.jfranklin.2018.12.020 doi: 10.1016/j.jfranklin.2018.12.020
    [29] S. Ling, H. Shi, H. Wang, P. X. Liu, Exponential synchronization of delayed coupled neural networks with delay-compensatory impulsive control, ISA Trans., 144 (2024), 133–144. https://doi.org/10.1016/j.isatra.2023.11.015 doi: 10.1016/j.isatra.2023.11.015
    [30] L. Wang, L. Li, Q. Cui, Z. Wang, Exponential synchronization of stochastic coupled neural networks with stochastic delayed impulsive effect, Neurocomputing, 604 (2024), 128262. https://doi.org/10.1016/j.neucom.2024.128262 doi: 10.1016/j.neucom.2024.128262
    [31] L. Shi, J. Li, H. Jiang, J. Wang, Quasi-synchronization of neural networks via non-fragile impulsive control: Multi-layer and memristor-based, Neurocomputing, 596 (2024), 128024. https://doi.org/10.1016/j.neucom.2024.128024 doi: 10.1016/j.neucom.2024.128024
    [32] W. Sun, Z. Tang, J. Feng, J. H. Park, Quasi-synchronization of heterogeneous neural networks with hybrid time delays via sampled-data saturating impulsive control, Chaos, Solitons Fractals, 182 (2024), 114788. https://doi.org/10.1016/j.chaos.2024.114788 doi: 10.1016/j.chaos.2024.114788
    [33] H. Fan, Y. Xiao, K. Shi, H. Wen, Y. Zhao, $\mu$-synchronization of coupled neural networks with hybrid delayed and non-delayed impulsive effects, Chaos, Solitons Fractals, 173 (2023), 113620. https://doi.org/10.1016/j.chaos.2023.113620 doi: 10.1016/j.chaos.2023.113620
    [34] L. Shi, J. Li, H. Jiang, J. Wang, Quasi-synchronization of multi-layer delayed neural networks with parameter mismatches via impulsive control, Chaos, Solitons Fractals, 175 (2023), 113994. https://doi.org/10.1016/j.chaos.2023.113994 doi: 10.1016/j.chaos.2023.113994
    [35] Y. Lin, A. Lindquist, Synchronization of nonlinear delayed semi-Markov jump neural networks via distributed delayed impulsive control, Syst. Control Lett., 174 (2023), 105489. https://doi.org/10.1016/j.sysconle.2023.105489 doi: 10.1016/j.sysconle.2023.105489
    [36] K. Udhayakumar, S. Shanmugasundaram, A. Kashkynbayev, K. Janani, R. Rakkiyappan, Saturated and asymmetric saturated impulsive control synchronization of coupled delayed inertial neural networks with time-varying delays, Appl. Math. Modell., 113 (2023), 528–544. https://doi.org/10.1016/j.apm.2022.09.011 doi: 10.1016/j.apm.2022.09.011
    [37] X. Zhang, C. Li, H. Li, J. Xu, Delayed distributed impulsive synchronization of coupled neural networks with mixed couplings, Neurocomputing, 507 (2022), 117–129. https://doi.org/10.1016/j.neucom.2022.07.045 doi: 10.1016/j.neucom.2022.07.045
    [38] X. Li, M. Bohner, Exponential synchronization of chaotic neural networks with mixed delays and impulsive effects via output coupling with delay feedback, Mathematical and Computer Modelling, 52.5-6 (2010) 643–653. https://doi.org/10.1016/j.mcm.2010.04.01 doi: 10.1016/j.mcm.2010.04.01
    [39] F. Jiang, J. Shen, X. Li, The LMI method for stationary oscillation of interval neural networks with three neuron activations under impulsive effects, Nonlinear Anal. Real World Appl., 14 (2013), 1404–1416. https://doi.org/10.1016/j.nonrwa.2012.10.004 doi: 10.1016/j.nonrwa.2012.10.004
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