Abstract
This paper aims to establish several synchronization criteria of competitive complex networks (CCNs) by using fixed-time (FDT) control. In CCNs, the variations of different nodes are diverse if they are influenced by external environment. Here, we design two types of controller to deal with the different variations of nodes. Meanwhile, these designed controllers guarantee the synchronization of the CCNs in a given time. The estimated settling time improves corresponding results in the literature. Furthermore, based on rigorous mathematical proof and the structured comparison system, several FDT synchronization criteria are obtained. Some comparisons are presented to show the advantages of these new theoretical results. The validity of our theoretical results is illustrated by numerical simulations.
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References
Xie Q, Chen G, Bollt EM (2002) Hybrid chaos synchronization and its application in information processing. Math Comput Model 35(1):145–163
Yang X, Liu Y, Cao J, Rutkowski L (2020) Synchronization of coupled time-delay neural networks with mode-dependent average dwell time switching. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.2968342
Volos CK, Kyprianidis IM, Stouboulos IN (2013) Image encryption process based on chaotic synchronization phenomena. Signal Process 93(5):1328–1340
Yang X, Xiaodi Li X, Lu J, Cheng Z (2020) Synchronization of time-delayed complex networks with switching topology via hybrid actuator fault and impulsive effects control. IEEE Trans Cybern 50(9):4043–4052
Wen G, Yu W, Hu G, Cao J, Yu X (2015) Pinning synchronization of directed networks with switching topologies: a multiple lyapunov functions approach. IEEE Trans Neural Netw Learn Syst 26(12):3239–3250
Yang X, Xu C, Feng J, Lu J (2018) General synchronization criteria for nonlinear Markovian systems with random delays. J Frankl Inst 355:1394–1410
Zhang W, Yang S, Li C, Li H (2020) Finite-time synchronization of delayed memristive neural networks via 1-norm-based analytical approach. Neural Comput Applic 32:4951–4960
Boubellouta A, Boulkroune A (2019) Intelligent fractional-order control-based projective synchronization for chaotic optical systems. Soft Comput 23:5367–5384
Yang X, Wan X, Cheng Z, Cao J, Liu Y, Rutkowski L (2020) Synchronization of switched discrete-time neural networks via quantized output control with actuator fault. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.3017171
Zhang W, Yang X, Xu C, Feng J, Li C (2018) Finite-time synchronization of discontinuous neural networks with delays and mismatched parameters. IEEE Trans Neural Netw Learn Syst 29(8):3761–3771
Yang X, Cao J (2014) Hybrid adaptive and impulsive synchronization of uncertain complex networks with delays and general uncertain perturbations. Appl Math Comput 227:480–493
Yang S, Li C, Huang T (2017) Synchronization of coupled memristive chaotic circuits via state-dependent impulsive control. Nonlinear Dyn 88:115–129
Zhang W, Li H, Li C, Li Z, Yang X (2019) Fixed-time synchronization criteria for complex networks via quantized pinning control. ISA Trans 91(2019):151–156
Xiong X, Yang X, Cao J, Tang R (2018) Finite-time control for a class of hybrid systems via quantized intermittent control. Sci China Inf Sci. https://doi.org/10.1007/s11432-018-2727-5
Zhou Y, Wan X, Huang C, Yang X (2020) Finite-time stochastic synchronization of dynamic networks with nonlinear coupling strength via quantized intermittent control. Appl Math Comput 376:125157
Boubellouta A, Zouari F, Boulkroune A (2019) Intelligent fuzzy controller for chaos synchronization of uncertain fractional-order chaotic systems with input nonlinearities. Int J Gen Syst 48(3):211–234
Yang X, Cheng Z, Li X, Ma T (2019) Exponential synchronization of coupled neutral-type neural networks with mixed delays via quantized output control. J Frankl Inst 356(15):8138–8153
Aghababa MP, Khanmohammadi S, Alizadeh G (2011) Finite-time synchronization of two different chaotic systems with unknown parameters via sliding mode technique. Appl Math Modell 35(6):3080–3091
Polyakov A, Efimov D, Perruquetti W (2015) Finite-time and fixed-time stabilization: Implicit Lyapunov function approach. Automatica 51:332–340
Xu C, Yang X, Lu J, Feng J, Alsaadi FE, Hayat T (2017) Finite-time synchronization of networks via quantized intermittent pinning control. IEEE Trans Cybern 48(10):3021–3027
Tang R, Yang X, Wan X (2019) Finite-time cluster synchronization for a class of fuzzy cellular neural networks via non-chattering quantized controllers. Neural Netw 113:79–90
Polyakov A (2012) Nonlinear feedback design for fixed-time stabilization of linear control systems. IEEE Trans Autom Control 57(8):2106–2110
Cao J, Li R (2017) Fixed-time synchronization of delayed memristor-based recurrent neural networks. Sci China Inf Sci 60(3):032201
Liu X, Cao J, Yu W, Song Q (2016) Nonsmooth finite-time synchronization of switched coupled neural networks. IEEE Trans Cybern 46(10):2360–2371
Xing W, Shi P, Song H, Zhao Y, Li L (2019) Global pinning synchronization of stochastic delayed complex networks. Inf Sci 490:113–125
Khanzadeh A, Pourgholi M (2017) Fixed-time sliding mode controller design for synchronization of complex dynamical networks. Nonlinear Dyn 88:2637–2649
Yang X, Lam J, Ho DWC, Feng Z (2017) Fixed-time synchronization of complex networks with impulsive effects via nonchattering control. IEEE Trans Autom Control 62(11):5511–5521
Xu Y, Wu X, Li N, Liu L, Xie C, Li C (2019) Fixed-time synchronization of complex networks with a simpler nonchattering controller. IEEE Trans Circuits Syst II. https://doi.org/10.1109/TCSII.2019.2920035
Liu X, Chen T (2018) Finite-time and fixed-time cluster synchronization with or without pinning control. IEEE Trans Cybern 48(1):240–252
Zhang W, Yang X, Li C (2019) Fixed-time stochastic synchronization of complex networks via continuous control. IEEE Trans Cybern 49(8):3099–3104
Aouiti C, Assali EA, Chérif F, Zeglaoui A (2019) Fixed-time synchronization of competitive neural networks with proportional delays and impulsive effect. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04654-3
Aouiti C, Miaadi F (2020) A new fixed-time stabilization approach for neural networks with time-varying delays. Neural Comput Appl 32:3295–3309
Ni J, Liu L, Liu C, Hu X, Li S (2017) Fast fixed-time nonsingular terminal sliding model control and its application to chaos supperession in power system. IEEE Trans Circuits Syst II 64(2):151–155
Xu Y, Meng D, Xie C, You G, Zhou W (2018) A class of fast fixed-time synchronization control for the delayed neural network. J Frankl Inst 355:164–176
Gao J, Fu Z, Zhang S (2019) Adaptive fixed-time attitude tracking control for rigid spacecraft with actuator faults. IEEE Trans Ind Electron 66(9):7141–7149
Sun H, Hou L, Li C (2019) Synchronization of single-degree-of-freedom oscillators via neural network based on fixed-time terminal sliding mode control scheme. Neural Comput Appl 31:6365–6372
Wang X, She K, Zhong S, Yang H (2019) Lag synchronization analysis of general complex networks with multiple time-varying delays via pinning control strategy. Neural Comput Appl 31:43–53
Shi Y, Zhu P (2014) Synchronization of memristive competitive neural networks with different time scales. Neural Comput Appl 25:1163–1168
Zhou W, Wang T, Mou J (2012) Synchronization control for the competitive complex networks with time delay and stochastic effects. Commun Nonlinear Sci Numer Simul 17:3417–3426
Khalil HK, Grizzle JW (2002) Nonlinear systems. Prentice-Hall, Upper Saddle River
Acknowledgements
This work was jointly supported by the National Natural Science Foundation of China (NSFC) (Nos. 61673078, 61903052, 62003065), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202000510), the Basic and Frontier Research Project of Chongqing (No. cstc2018jcyjAX0369), and the Scientific Research Fund of Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering (No. 2018MMAEZD01), Foundation Project of Chongqing Normal University (No. 20XLB003).
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Zhang, W., Yang, X., Yang, S. et al. Fixed-time control of competitive complex networks. Neural Comput & Applic 33, 7943–7951 (2021). https://doi.org/10.1007/s00521-020-05539-6
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DOI: https://doi.org/10.1007/s00521-020-05539-6