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Articles

Finite-time synchronisation of delayed fractional-order coupled neural networks

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Pages 2597-2611 | Received 28 Dec 2021, Accepted 14 Apr 2022, Published online: 04 May 2022

References

  • Braga, J., Almeida, M., Braga, A., & Belchior, J. (2000). Hopfield neural network model for calculating the potential energy function from second virial data. Chemical Physics, 260(3), 347–352. https://doi.org/10.1016/S0301-0104(00)00218-4
  • Chen, B., & Chen, J. (2015). Razumikhin-type stability theorems for functional fractional-order differential systems and applications. Applied Mathematics and Computation, 254, 63–69. https://doi.org/10.1016/j.amc.2014.12.010
  • Chen, J., Chen, B., & Zeng, Z. (2018). O(t−α)-synchronization and Mittag-Leffler synchronization for the fractional-order memristive neural networks with delays and discontinuous neuron activations. Neural Networks, 100, 10–24. https://doi.org/10.1016/j.neunet.2018.01.004
  • Ding, X., Cao, J., Zhao, X., & Alsaadi, F. E. (2017). Mittag-Leffler synchronization of delayed fractional-order bidirectional associative memory neural networks with discontinuous activations: State feedback control and impulsive control schemes. Proceedings of Society A – Mathematical Physical and Engineering Sciences, 473(2204), 1–21. https://doi.org/10.1098/rspa.2017.0322
  • Ding, Z., Zeng, Z., & Wang, L. (2018). Robust finite-time stabilization of fractional-order neural networks with discontinuous and continuous activation functions under uncertainty. IEEE Transactions on Neural Networks and Learning Systems, 29(5), 1477–1490. https://doi.org/10.1109/TNNLS.2017.2675442
  • Gao, Z., He, Y., & Wu, M. (2019). Improved stability criteria for the neural networks with time-varying delay via new augmented Lyapunov-Krasovskii functional. Applied Mathematics and Computation, 349, 258–269. https://doi.org/10.1016/j.amc.2018.12.026
  • Gupta, M., Jin, L., & Homma, N. (2004). Static and dynamic neural networks: From fundamentals to advanced theory. John Wiley and Sons.
  • Hu, T., He, Z., Zhang, X., & Zhong, S. (2020). Finite-time stability for fractional-order complex-valued neural networks with time delay. Applied Mathematics and Computation, 365https://doi.org/10.1016/j.amc.2019.124715
  • Huang, Y., Hou, J., Ren, S., & Yang, E. (2019). Passivity and synchronization of coupled complex-valued memristive neural networks. In 2019 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 2152–2159). https://doi.org/10.1109/SSCI44817.2019.9002792
  • Li, L., Cao, J., Jiang, H., & Alsaedi, A. (2018). Graph theory-based finite-time synchronization of fractional-order complex dynamical networks. Journal of the Franklin Institute, 355(13), 5771–5789. https://doi.org/10.1016/j.jfranklin.2018.05.039
  • Li, X., Fang, J., Zhang, W., & Li, H. (2018). Finite-time synchronization of fractional-order memristive recurrent neural networks with discontinuous activation functions. Neurocomputing, 316, 284–293. https://doi.org/10.1016/j.neucom.2018.08.003
  • Liu, G. P. (2001). Nonlinear identification and control: A neural network approach. Springer.
  • Lu, R., Yu, W., Lü, J., & Xue, A. (2014). Synchronization on complex networks of networks. IEEE Transactions on Neural Networks and Learning Systems, 25(11), 2110–2118. https://doi.org/10.1109/TNNLS.2014.2305443
  • Moon, Y., Park, P., Kwon, W., & Lee, Y. (2001). Delay dependent robust stabilization of uncertain state-delayed systems. International Journal of Control, 74(14), 1447–1455. https://doi.org/10.1080/00207170110067116
  • Podlubny, I.. (1999). Fractional differential equations. San Diego, CA: Academic Press.
  • Polyakov, A., Efimov, D., & Perruquetti, W. (2015). Finite-time and fixed-time stabilization: Implicit Lyapunov function approach. Automatica, 51, 332–340. https://doi.org/10.1016/j.automatica.2014.10.082
  • Pratap, A., Raja, R., Cao, J., Rajchakit, G., & Fardoun, H. M. (2019). Stability and synchronization criteria for fractional order competitive neural networks with time delays: An asymptotic expansion of Mittag Leffler function. Journal of the Franklin Institute, 356(4), 2212–2239. https://doi.org/10.1016/j.jfranklin.2019.01.017
  • Pratap, A., Raja, R., Cao, J., Rihan, F. A., & Seadawy, A. R. (2020). Quasi-pinning synchronization and stabilization of fractional order BAM neural networks with delays and discontinuous neuron activations. Chaos, Solitons & Fractals, 131. https://doi.org/10.1016/j.chaos.2019.109491
  • Seuret, A., & Gouaisbaut, F. (2013). Wirtinger-based integral inequality: Application to time-delay systems. Automatica, 49, 2860–2866. https://doi.org/10.1016/j.automatica.2013.05.030
  • Shi, M., Yu, Y., & Xu, Q. (2019). Delay-dependent consensus condition for a class of fractional-order linear multi-agent systems with input time-delay. International Journal of Systems Science, 50(4), 669–678. https://doi.org/10.1080/00207721.2019.1567865
  • Sowmiya, C., Raja, R., Cao, J., Rajchakit, G., & Alsaedi, A. (2017). Enhanced robust finite-time passivity for Markovian jumping discrete-time BAM neural networks with leakage delay. Advances in Difference Equations, 318(1), 1–28, https://doi.org/10.1186/s13662-017-1378-9
  • Wang, F., Chen, D., Zhang, X., & Wu, Y. (2017). Finite-time stability of a class of nonlinear fractional-order system with the discrete time delay. International Journal of Systems Science, 48(5), 984–993. https://doi.org/10.1080/00207721.2016.1226985
  • Wang, F., Liu, X., Tang, M., & Chen, L. (2019). Further results on stability and synchronization of fractional-order Hopfield neural networks. Neurocomputing, 346, 12–19. https://doi.org/10.1016/j.neucom.2018.08.089
  • Xiao, J., Zhong, S., Li, Y., & Xu, F. (2017). Finite-time Mittag-Leffler synchronization of fractional-order memristive BAM neural networks with time delays. Neurocomputing, 219, 431–439. https://doi.org/10.1016/j.neucom.2016.09.049
  • Yang, S., Yu, J., Hu, C., & Jiang, H. (2021). Finite-time synchronization of memristive neural networks with fractional-order. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(6), 3739–3750. https://doi.org/10.1109/TSMC.2019.2931046
  • Zhang, S., Guo, Y., Wang, S., Hu, X., & Xie, X. (2019). Input-output finite-time stability of discrete-time systems under finite-time boundedness. International Journal of Systems Science, 50(2), 419–431. https://doi.org/10.1080/00207721.2018.1554170
  • Zhang, X. M., Han, Q. L., Ge, X., & Zhang, B. L. (2021). Delay-variation-dependent criteria on extended dissipativity for discrete-time neural networks with time-varying delay. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3105591
  • Zhang, X. M., Han, Q. L., & Wang, J. (2018). Admissible delay upper bounds for global asymptotic stability of neural networks with time-varying delays. IEEE Transactions on Neural Networks and Learning Systems, 29(11), 5319–5329. https://doi.org/10.1109/TNNLS.2018.2797279
  • Zhang, X. M., Han, Q. L., & Zeng, Z. (2018). Hierarchical type stability criteria for delayed neural networks via canonical Bessel-Legendre inequalities. IEEE Transactions on Cybernetics, 48(5), 1660–1671. https://doi.org/10.1109/TCYB.2017.2776283
  • Zhang, Y., Wu, H., & Cao, J. (2020). Group consensus in finite time for fractional multiagent systems with discontinuous inherent dynamics subject to Hölder growth. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2020.3023704
  • Zhang, Z., & Cao, J. (2019). Novel finite-time synchronization criteria for inertial neural networks with time delays via integral inequality method. IEEE Transactions on Neural Networks and Learning Systems, 30(5), 1476–1485. https://doi.org/10.1109/TNNLS.2018.2868800
  • Zhao, Y., Ren, S., & Kurths, J. (2021). Synchronization of coupled memristive competitive BAM neural networks with different time scales. Neurocomputing, 427, 110–117. https://doi.org/10.1016/j.neucom.2020.11.023
  • Zhou, L., Wang, C., Du, S., & Zhou, L. (2017). Cluster synchronization on multiple nonlinearly coupled dynamical subnetworks of complex networks with nonidentical nodes. IEEE Transactions on Neural Networks and Learning Systems, 28(3), 570–583. https://doi.org/10.1109/TNNLS.2016.2547463

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