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Regular Articles

Global quasi-synchronisation of fuzzy cellular neural networks with time varying delay and interaction terms

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Pages 2679-2693 | Received 31 Dec 2021, Accepted 21 Mar 2022, Published online: 13 Apr 2022

References

  • Abdurahman, A., Jiang, H., & Teng, Z. (2016). Finite-time synchronization for fuzzy cellular neural networks with time-varying delays. Fuzzy Sets and Systems, 297, 96–111. https://doi.org/10.1016/j.fss.2015.07.009
  • Arunkumar, A., Sakthivel, R., Mathiyalagan, K., & Park, J. H. (2014). Robust stochastic stability of discrete-time fuzzy Markovian jump neural networks. ISA Transactions, 53(4), 1006–1014. https://doi.org/10.1016/j.isatra.2014.05.002
  • Chua, L. O., & Yang, L. (1988a). Cellular neural networks: Theory. IEEE Transactions on Circuits and Systems, 35(10), 1257–1272. https://doi.org/10.1109/31.7600
  • Chua, L. O., & Yang, L. (1988b). Cellular neural networks: Applications. IEEE Transactions on Circuits and Systems, 35(10), 1273–1290. https://doi.org/10.1109/TCS.31
  • Ding, W., & Han, M. (2008). Synchronization of delayed fuzzy cellular neural networks based on adaptive control. Physics Letters A, 372(26), 4674–4681. https://doi.org/10.1016/j.physleta.2008.04.053
  • Du, F., & Lu, J.-G. (2021). New criterion for finite-time synchronization of fractional order memristor-based neural networks with time delay. Applied Mathematics and Computation, 389(8), 125616. https://doi.org/10.1016/j.amc.2020.125616
  • Duan, L., Wei, H., & Huang, L. (2019). Finite-time synchronization of delayed fuzzy cellular neural networks with discontinuous activations. Fuzzy Sets and Systems, 361, 56–70. https://doi.org/10.1016/j.fss.2018.04.017
  • Fan, Y., Huang, X., Li, Y., Xia, J., & Chen, G. (2018). Aperiodically intermittent control for quasi-synchronization of delayed memristive neural networks: an interval matrix and matrix measure combined method. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(11), 2254–2265. https://doi.org/10.1109/TSMC.6221021
  • Fan, Y., Huang, X., Wang, Z., & Li, Y. (2018). Improved quasi-synchronization criteria for delayed fractional-order memristor-based neural networks via linear feedback control. Neurocomputing, 306(11), 68–79. https://doi.org/10.1016/j.neucom.2018.03.060
  • Feng, X., Zhang, F., & Wang, W. (2011). Global exponential synchronization of delayed fuzzy cellular neural networks with impulsive effects. Chaos, Solitons & Fractals, 44(1–3), 9–16. https://doi.org/10.1016/j.chaos.2010.10.003
  • Gan, Q., Xu, R., & Yang, P. (2012). Synchronization of non-identical chaotic delayed fuzzy cellular neural networks based on sliding mode control. Communications in Nonlinear Science and Numerical Simulation, 17(1), 433–443. https://doi.org/10.1016/j.cnsns.2011.05.014
  • Harrer, H., & Nossek, J. A. (1992). Discrete-time cellular neural networks. International Journal of Circuit Theory and Applications, 20(5), 453–467. https://doi.org/10.1002/(ISSN)1097-007X
  • He, W., Qian, F., Han, Q.-L., & Cao, J. (2011). Lag quasi-synchronization of coupled delayed systems with parameter mismatch. IEEE Transactions on Circuits and Systems I: Regular Papers, 58(6), 1345–1357. https://doi.org/10.1109/TCSI.2010.2096116
  • Huang, C., Ho, D. W. C., Lu, J., & Kurths, J. (2014). Pinning synchronization in T–S fuzzy complex networks with partial and discrete-time couplings. IEEE Transactions on Fuzzy Systems, 23(4), 1274–1285. https://doi.org/10.1109/TFUZZ.2014.2350534
  • Huang, Z., Cao, J., Li, J., & Bin, H. (2019). Quasi-synchronization of neural networks with parameter mismatches and delayed impulsive controller on time scales. Nonlinear Analysis: Hybrid Systems, 33(2), 104–115. https://doi.org/10.1016/j.nahs.2019.02.005
  • Kalpana, M., Balasubramaniam, P., & Ratnavelu, K. (2015). Direct delay decomposition approach to synchronization of chaotic fuzzy cellular neural networks with discrete, unbounded distributed delays and Markovian jumping parameters. Applied Mathematics and Computation, 254(43), 291–304. https://doi.org/10.1016/j.amc.2014.12.133
  • Kandasamy, U., Li, X., & Rajan, R. (2019). Quasi-synchronization and bifurcation results on fractional-order quaternion-valued neural networks. IEEE Transactions on Neural Networks and Learning Systems, 31(10), 4063–4072. https://doi.org/10.1109/TNNLS.5962385
  • Kumar, A., Das, S., Yadav, V. K., Cao, J., & Huang, C. (2021). Synchronizations of fuzzy cellular neural networks with proportional time-delay. AIMS Mathematics, 6(10), 10620–10641. https://doi.org/10.3934/math.2021617
  • Kumar, A., Das, S., Yadav, V. K., & Rajeev, (2021). Global quasi-synchronization of complex-valued recurrent neural networks with time-varying delay and interaction terms. Chaos, Solitons & Fractals, 152(3–4), 111323. https://doi.org/10.1016/j.chaos.2021.111323
  • Kumar, A., Yadav, V. K., & Das, S. (2021). Global exponential stability of Takagi–Sugeno fuzzy Cohen–Grossberg neural network with time-varying delays. IEEE Control Systems Letters, 6, 325–330. https://doi.org/10.1109/LCSYS.2021.3073962.
  • Kumar, U., Das, S., Huang, C., & Cao, J. (2020). Fixed-time synchronization of quaternion-valued neural networks with time-varying delay. Proceedings of the Royal Society A, 476(2241), 20200324. https://doi.org/10.1098/rspa.2020.0324
  • Li, L., Xu, R., Gan, Q., & Lin, J. (2020). Synchronization of a novel model for memristive neural networks via sliding mode control. ISA Transactions, 106(5), 31–39. https://doi.org/10.1016/j.isatra.2020.07.012
  • Li, R., Gao, X., & Cao, J. (2019). Quasi-state estimation and quasi-synchronization control of quaternion-valued fractional-order fuzzy memristive neural networks: Vector ordering approach. Applied Mathematics and Computation, 362, 124572. https://doi.org/10.1016/j.amc.2019.124572
  • Liu, X., & Chen, T. (2016). Finite-time and fixed-time cluster synchronization with or without pinning control. IEEE Transactions on Cybernetics, 48(1), 240–252. https://doi.org/10.1109/TCYB.2016.2630703
  • Liu, X., Li, P., Xu, Y., & Liu, Y. (2014). Quasi-synchronization for delayed systems with parameter mismatches via aperiodically intermittent control. In The 2014 2nd International Conference on Systems and Informatics (ICSAI 2014) (pp. 33–38). IEEE. https://doi.org/10.1109/ICSAI.2014.7009255
  • Liu, Y., & Tang, W. (2004). Exponential stability of fuzzy cellular neural networks with constant and time-varying delays. Physics Letters A, 323(3–4), 224–233. https://doi.org/10.1016/j.physleta.2004.01.064
  • Long, S., Song, Q., Wang, X., & Li, D. (2012). Stability analysis of fuzzy cellular neural networks with time delay in the leakage term and impulsive perturbations. Journal of the Franklin Institute, 349(7), 2461–2479. https://doi.org/10.1016/j.jfranklin.2012.05.009
  • Lu, J., Ding, C., Lou, J., & Cao, J. (2015). Outer synchronization of partially coupled dynamical networks via pinning impulsive controllers. Journal of the Franklin Institute, 352(11), 5024–5041. https://doi.org/10.1016/j.jfranklin.2015.08.016
  • Ma, W., Li, C., Wu, Y., & Wu, Y. (2017). Synchronization of fractional fuzzy cellular neural networks with interactions. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27(10), 103106. https://doi.org/10.1063/1.5006194
  • Mani, P., Rajan, R., Shanmugam, L., & Joo, Y. H. (2019). Adaptive control for fractional order induced chaotic fuzzy cellular neural networks and its application to image encryption. Information Sciences, 491(1), 74–89. https://doi.org/10.1016/j.ins.2019.04.007
  • Pan, L. (2020). Stochastic quasi-synchronization of delayed neural networks: Pinning impulsive scheme. Neural Processing Letters, 51(1), 947–962. https://doi.org/10.1007/s11063-019-10118-5
  • Pecora, L. M., & Carroll, T. L. (1990). Synchronization in chaotic systems. Physical Review Letters, 64(8), 821–824. https://doi.org/10.1103/PhysRevLett.64.821
  • Perruquetti, W., Floquet, T., & Moulay, E. (2008). Finite-time observers: Application to secure communication. IEEE Transactions on Automatic Control, 53(1), 356–360. https://doi.org/10.1109/TAC.2007.914264
  • Polyakov, A., Efimov, D., & Perruquetti, W. (2015). Finite-time and fixed-time stabilization: Implicit Lyapunov function approach. Automatica, 51(4), 332–340. https://doi.org/10.1016/j.automatica.2014.10.082
  • Rakkiyappan, R., Sakthivel, N., Park, J. H., & Kwon, O. M. (2013). Sampled-data state estimation for Markovian jumping fuzzy cellular neural networks with mode-dependent probabilistic time-varying delays. Applied Mathematics and Computation, 221, 741–769. https://doi.org/10.1016/j.amc.2013.07.007
  • Ratnavelu, K., Manikandan, M., & Balasubramaniam, P. (2015). Synchronization of fuzzy bidirectional associative memory neural networks with various time delays. Applied Mathematics and Computation, 270, 582–605. https://doi.org/10.1016/j.amc.2015.07.061
  • Roska, T., & Chua, L. O. (1992). Cellular neural networks with non-linear and delay-type template elements and non-uniform grids. International Journal of Circuit Theory and Applications, 20(5), 469–481. https://doi.org/10.1002/(ISSN)1097-007X
  • Ruan, X., & Wu, A. (2017). Multi-quasi-synchronization of coupled fractional-order neural networks with delays via pinning impulsive control. Advances in Difference Equations, 2017(1), 1–19. https://doi.org/10.1186/s13662-017-1417-6
  • Syed Ali, M., Vadivel, R., & Saravanakumar, R. (2018). Design of robust reliable control for TS fuzzy Markovian jumping delayed neutral type neural networks with probabilistic actuator faults and leakage delays: An event-triggered communication scheme. ISA Transactions, 77, 30–48. https://doi.org/10.1016/j.isatra.2018.01.030
  • Tang, R., Yang, X., & Wan, X. (2019). Finite-time cluster synchronization for a class of fuzzy cellular neural networks via non-chattering quantised controllers. Neural Networks, 113(4), 79–90. https://doi.org/10.1016/j.neunet.2018.11.010
  • Tang, Z., Park, J. H., & Feng, J. (2017). Impulsive effects on quasi-synchronization of neural networks with parameter mismatches and time-varying delay. IEEE Transactions on Neural Networks and Learning Systems, 29(4), 908–919. https://doi.org/10.1109/TNNLS.2017.2651024
  • Wan, Y., & Cao, J. (2015). Periodicity and synchronization of coupled memristive neural networks with supremums. Neurocomputing, 159(5), 137–143. https://doi.org/10.1016/j.neucom.2015.02.007
  • Wan, Y., Cao, J., Wen, G., & Yu, W. (2016). Robust fixed-time synchronization of delayed Cohen–Grossberg neural networks. Neural Networks, 73(3), 86–94. https://doi.org/10.1016/j.neunet.2015.10.009
  • Xing, Z., & Peng, J. (2012). Exponential lag synchronization of fuzzy cellular neural networks with time-varying delays. Journal of the Franklin Institute, 349(3), 1074–1086. https://doi.org/10.1016/j.jfranklin.2011.12.008
  • Yang, T., & Yang, L.-B. (1996). The global stability of fuzzy cellular neural network. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 43(10), 880–883. https://doi.org/10.1109/81.538999
  • Yang, W., Yu, W., Cao, J., Alsaadi, F. E., & Hayat, T. (2018). Global exponential stability and lag synchronization for delayed memristive fuzzy Cohen–Grossberg bam neural networks with impulses. Neural Networks, 98, 122–153. https://doi.org/10.1016/j.neunet.2017.11.001
  • Yu, F., & Jiang, H. (2011). Global exponential synchronization of fuzzy cellular neural networks with delays and reaction–diffusion terms. Neurocomputing, 74(4), 509–515. https://doi.org/10.1016/j.neucom.2010.08.017
  • Yuan, K., Cao, J., & Deng, J. (2006). Exponential stability and periodic solutions of fuzzy cellular neural networks with time-varying delays. Neurocomputing, 69(13–15), 1619–1627. https://doi.org/10.1016/j.neucom.2005.05.011
  • Zhang, X., Niu, P., Hu, X., Ma, Y., & Li, G. (2019). Global quasi-synchronization and global anti-synchronization of delayed neural networks with discontinuous activations via non-fragile control strategy. Neurocomputing, 361(4), 1–9. https://doi.org/10.1016/j.neucom.2019.07.041
  • Zheng, M., Li, L., Peng, H., Xiao, J., Yang, Y., & Zhao, H. (2016). Finite-time stability and synchronization for memristor-based fractional-order Cohen–Grossberg neural network. The European Physical Journal B, 89(9), 204. https://doi.org/10.1140/epjb/e2016-70337-6
  • Zheng, M., Li, L., Peng, H., Xiao, J., Yang, Y., & Zhao, H. (2017). Finite-time stability analysis for neutral-type neural networks with hybrid time-varying delays without using Lyapunov method. Neurocomputing, 238(6), 67–75. https://doi.org/10.1016/j.neucom.2017.01.037

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