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Almost periodic synchronization of fuzzy cellular neural networks with time-varying delays via state-feedback and impulsive control

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, we are concerned with drive-response synchronization for a class of fuzzy cellular neural networks with time varying delays. Based on the exponential dichotomy of linear differential equations, the Banach fixed point theorem and the differential inequality technique, we obtain the existence of almost periodic solutions of this class of networks. Then, we design a state feedback and an impulsive controller, and construct a suitable Lyapunov function to study the problem of global exponential almost periodic synchronization for the drive-response systems considered. At the end of the paper, we provide an example to verify the effectiveness of the theoretical results.
Rocznik
Strony
337--349
Opis fizyczny
Bibliogr. 50 poz., wykr.
Twórcy
autor
  • Department of Mathematics, Yunnan University, Kunming, Yunnan 650091, People’s Republic of China
autor
  • Department of Mathematics. Kunming University, Kunming, Yunnan 650214, People’s Republic of China
  • Department of Mathematics, Yunnan University, Kunming, Yunnan 650091, People’s Republic of China
Bibliografia
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  • [6] Cai, Z., Huang, L., Guo, Z., Zhang, L. and Wan, X. (2015). Periodic synchronization control of discontinuous delayed networks by using extended Filippov-framework, Neural Networks 68: 96–110.
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  • [9] Cao, J. and Liang, J. (2004). Boundedness and stability for Cohen–Grossberg neural network with time-varying delays, Journal of Mathematical Analysis and Applications 296(2): 665–685.
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  • [11] Ding,W. and Han, M. (2008). Synchronization of delayed fuzzy cellular neural networks based on adaptive control, Physics Letters A 372(26): 4674–4681.
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  • [17] Hong, H. (2014). Periodic synchronization and chimera in conformist and contrarian oscillators, Physical Review E 89(6): 1–37.
  • [18] Hu, C., Yu, J., Jiang, H. and Teng, Z. (2010). Exponential stabilization and synchronization of neural networks with time-varying delays via periodically intermittent control, Nonlinearity 23(10): 2369–2391.
  • [19] Huang, Z. (2017a). Almost periodic solutions for fuzzy cellular neural networks with multi-proportional delays, International Journal of Machine Learning and Cybernetics 28(4): 1–9.
  • [20] Huang, Z. (2017b). Almost periodic solutions for fuzzy cellular neural networks with time-varying delays, Neural Computing and Applications 28(8): 2313–2320.
  • [21] Li, Y., Chen, X. and Zhao, L. (2009). Stability and existence of periodic solutions to delayed Cohen–Grossberg BAM neural networks with impulses on time scales, Neurocomputing 72(7–9): 1621–1630.
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  • [23] Li, Y., Li, B., Yao, S. and Xiong, L. (2018a). The global exponential pseudo almost periodic synchronization of quaternion-valued cellular neural networks with time-varying delays, Neurocomputing 303: 75–87.
  • [24] Li, Y., Meng, X. and Ye, Y. (2018b). Almost periodic synchronization for quaternion-valued neural networks with time-varying delays, Complexity 2018, Article ID: 6504590.
  • [25] Li, Y., Wang, H. and Meng, X. (2018c). Almost automorphic synchronization of quaternion-valued high-order Hopfield neural networks with time-varying and distributed delays, IMA Journal of Mathematical Control and Information: dny015, DOI:10.1093/imamci/dny015.
  • [26] Li, Y. and Wang, C. (2013). Existence and global exponential stability of equilibrium for discrete-time fuzzy BAM neural networks with variable delays and impulses, Fuzzy Sets and Systems 217: 62–79.
  • [27] Li, Y. and Zhang, T. (2009). Global exponential stability of fuzzy interval delayed neural networks with impulses on time scales, International Journal of Neural Systems 19(06): 449–456.
  • [28] Lin, Y. and Zhang, Y. (2018). Synchronization of stochastic impulsive discrete-time delayed networks via pinning control, Neurocomputing 286: 31–40.
  • [29] Long, S. and Xu, D. (2011). Stability analysis of stochastic fuzzy cellular neural networks with time-varying delays, Neurocomputing 69(14–15): 2385–2391.
  • [30] Lu, J., Ho, D.W.C., Cao, J. and Kurths, J. (2013). Single impulsive controller for globally exponential synchronization of dynamical networks, Nonlinear Analysis: Real World Applications 14(1): 581–593.
  • [31] Lu, X., Zhang, X. and Liu, Q. (2018). Finite-time synchronization of nonlinear complex dynamical networks on time scales via pinning impulsive control, Neurocomputing 275: 2104–110.
  • [32] Pan, L. and Cao, J. (2011). Anti-periodic solution for delayed cellular neural networks with impulsive effects, Nonlinear Analysis: Real World Applications 12(6): 3014–3027.
  • [33] Park, J.H. (2009). Synchronization of cellular neural networks of neutral type via dynamic feedback controller, Chaos Solitons & Fractals 42(3): 1299–1304.
  • [34] Pecora, L.M. and Carroll, T.L. (1990). Synchronization in chaotic systems, Physical Review Letters 64(8): 821–824.
  • [35] Sen, M.D.L. (2006). Stability of impulsive time-varying systems and compactness of the operators mapping the input space into the state and output spaces, Journal of Mathematical Analysis and Applications 321(2): 621–650.
  • [36] Stamov, G.T. (2012). Almost Periodic Solutions for Impulsive Differential Equations, Springer, Berlin.
  • [37] Tang, Z., Park, J.H. and Feng, J. (2018a). 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.
  • [38] Tang, Z., Park, J.H., Wang, Y. and Feng, J. (2018b). Distributed impulsive quasi-synchronization of Lur’e networks with proportional delay, IEEE Transactions on Cybernetics, 49(8): 3105–3115, DOI:10.1109/TCYB.2018.2839178.
  • [39] Wang, W. (2018). Finite-time synchronization for a class of fuzzy cellular neural networks with time-varying coefficients and proportional delays, Fuzzy Sets and Systems 338: 40–49.
  • [40] Wu, H., Li, R., Zhang, X. and Yao, R. (2015). Adaptive finite-time complete periodic synchronization of memristive neural networks with time delays, Neural Processing Letters 42(3): 563–583.
  • [41] Xia, Y., Cao, J. and Cheng, S.S. (2007). Global exponential stability of delayed cellular neural networks with impulses, Neurocomputing 70(13–15): 2495–2501.
  • [42] Xu, D. and Yang, Z. (2005). Impulsive delay differential inequality and stability of neural networks, Journal of Mathematical Analysis and Applications 305(1): 107–120.
  • [43] Yang, H., Wang, X., Zhong, S. and Shu, L. (2018). Synchronization of nonlinear complex dynamical systems via delayed impulsive distributed control, Applied Mathematics and Computation 320: 75–85.
  • [44] Yang, T. (2001). Impulsive Control Theory, Springer, Berlin.
  • [45] Yang, T. and 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.
  • [46] Yang, W., Yu, W., Cao, J., Alsaadi, F.E. and Hayat, T. (2017). Global exponential stability and lag synchronization for delayed memristive fuzzy Cohen–Grossberg BAM neural networks with impulses, Neural Networks 98: 122–153.
  • [47] Yang, X., Cao, J. and Ho, D. W.C. (2015). Exponential synchronization of discontinuous neural networks with time-varying mixed delays via state feedback and impulsive control, Cognitive Neurodynamics 9(2): 113–128.
  • [48] Yuan, K., Cao, J. and Deng, J. (2006). Exponential stability and periodic solutions of fuzzy cellular neural networks with time-varying delays, Neurocomputing 69(13–15): 1619–1627.
  • [49] Yuan, K., Fei, S. and Cao, J. (2014). Partial synchronization of the distributed parameter system with time delay via fuzzy control, IMA Journal of Mathematical Control and Information 31(1): 51–72.
  • [50] Zhang, B., Deng, F., Xie, S. and Luo, S. (2018). Exponential synchronization of stochastic time-delayed memristor-based neural networks via distributed impulsive control, Neurocomputing 286: 41–50.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a7e67aac-624e-4f01-8dca-41f85b2f31ce
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