Abstract This paper deals with the cluster exponential synchronization of a class of complex networks with hybrid coupling and time-varying delay. Through constructing an appropriate Lyapunov-Krasovskii functional and applying the theory of the Kronecker product of matrices and the linear matrix inequality (LMI) technique, several novel sufficient conditions for cluster exponential synchronization are obtained. These cluster exponential synchronization conditions adopt the bounds of both time delay and its derivative, which are less conservative. Finally, the numerical simulations are performed to show the effectiveness of the theoretical results.
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61074073 and 61034005), the Fundamental Research Funds for the Central Universities of China (Grant No. N110504001), and the Open Project of the State Key Laboratory of Management and Control for Complex Systems, China (Grant No. 20110107).
Wang Jun-Yi (王军义), Zhang Hua-Guang (张化光), Wang Zhan-Shan (王占山), Liang Hong-Jing (梁洪晶) Cluster exponential synchronization of a class of complex networks with hybrid coupling and time-varying delay 2013 Chin. Phys. B 22 090504
[1]
Watts D J and Strogatz S H 1998 Nature 393 440
[2]
Barabási A L and Albert R 1999 Science 286 509
[3]
Wang X F and Chen G R 2003IEEE Circ. Syst. Mag. 3 6
[4]
Wang X F and Chen G R 2002 IEEE Trans. Circ. Syst. I: Fund. Theory Appl. 49 54
[5]
Wu C W and Chua L O 1995 IEEE Trans. Circ. Syst. I: Fund. Theory Appl. 42 430
[6]
Li Z and Chen G R 2004 Phys. Lett. A 324 166
[7]
Yu W W, Chen G R and Lü J H 2009 Automatica 45 429
[8]
Zhao J, Hill D J and Liu T 2009 Automatica 45 2502
[9]
Lu J Q, Ho D W C and Cao J D 2010 Automatica 46 1215
[10]
Zhang H G, Liu Z W, Huang G B and Wang Z S 2010 IEEE Trans.Neural Netw. 21 91
[11]
Yu W W, Cao J D and Lü J H 2008 SIAM J. Appl. Dyn. Syst. 7 108
[12]
Wang Z S, Zhang H G and Li P 2010 IEEE Trans. Syst. Man Cybern. B 40 1596
[13]
Wu Z G, Shi P, Su H Y and Chu J 2012 IEEE Trans. Neural Netw. Learn. Syst. 23 1368
[14]
Cao J D, Li P and Wang W W 2006 Phys. Lett. A 353 318
[15]
Wang Z D, Wang Y and Liu Y R 2010 IEEE Trans. Neural Netw. 21 11
[16]
Gong D W, Zhang H G, Wang Z S and Huang B N 2012 Neurocomputing 82 14
[17]
Zhang H G, Gong D W and Wang Z S 2011 Chin. Phys. B 20 040512
[18]
Cao J D, Chen G R and Li P 2008 IEEE Trans. Syst. Man Cybern. B 38 488
[19]
Song Q K 2009 Neurocomputing 72 3907
[20]
Cao J D and Li L L 2009 Neural Netw. 22 335
[21]
Ma Z J, Liu Z R and Zhang G 2006 Chaos 16 023103
[22]
Qin W X and Chen G R 2004 Physica D 197 375
[23]
Yao H X and Wang S G 2012 Chin. Phys. B 21 110506
[24]
Wu Z Y and Fu X C 2012 Commun. Nonlinear Sci. Numer. Simul. 17 1628
[25]
Wu J S, Jiao L C and Chen G R 2011 Chin. Phys. B 20 060503
[26]
Chen J L and Chen X H 2001 (Beijing: Tsinghua University Press)
[27]
Gu K Q, Kharitonov V L and Chen J 2003 (Boston, MA: Birkhäuser)
Characteristics of vapor based on complex networks in China Ai-Xia Feng(冯爱霞), Qi-Guang Wang(王启光), Shi-Xuan Zhang(张世轩), Takeshi Enomoto(榎本刚), Zhi-Qiang Gong(龚志强), Ying-Ying Hu(胡莹莹), and Guo-Lin Feng(封国林). Chin. Phys. B, 2022, 31(4): 049201.
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