Please wait a minute...
Chin. Phys. B, 2014, Vol. 23(5): 050509    DOI: 10.1088/1674-1056/23/5/050509
GENERAL Prev   Next  

Pinning sampled-data synchronization for complex networks with probabilistic coupling delay

Wang Jian-An (王健安), Nie Rui-Xing (聂瑞兴), Sun Zhi-Yi (孙志毅)
School of Electronics Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
Abstract  We deal with the problem of pinning sampled-data synchronization for a complex network with probabilistic time-varying coupling delay. The sampling period considered here is assumed to be less than a given bound. Without using the Kronecker product, a new synchronization error system is constructed by using the property of the random variable and input delay approach. Based on the Lyapunov theory, a delay-dependent pinning sampled-data synchronization criterion is derived in terms of linear matrix inequalities (LMIs) that can be solved effectively by using MATLAB LMI toolbox. Numerical examples are provided to demonstrate the effectiveness of the proposed scheme.
Keywords:  complex network      probabilistic time-varying coupling delay      sampled-data synchronization      pinning control  
Received:  18 August 2013      Revised:  18 November 2013      Accepted manuscript online: 
PACS:  05.45.Xt (Synchronization; coupled oscillators)  
  05.45.Gg (Control of chaos, applications of chaos)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61203049 and 61303020), the Natural Science Foundation of Shanxi Province of China (Grant No. 2013021018-3), and the Doctoral Startup Foundation of Taiyuan University of Science and Technology, China (Grant No. 20112010).
Corresponding Authors:  Wang Jian-An     E-mail:  wangjianan588@163.com
About author:  05.45.Xt; 05.45.Gg

Cite this article: 

Wang Jian-An (王健安), Nie Rui-Xing (聂瑞兴), Sun Zhi-Yi (孙志毅) Pinning sampled-data synchronization for complex networks with probabilistic coupling delay 2014 Chin. Phys. B 23 050509

[1] Albert R and Barabasi A L 2002 Rev. Mod. Phys. 74 47
[2] Newman M E 2003 SIAM. Rev. 45 167
[3] Wang X F and Chen G R 2003 IEEE Circuits Syst. Mag. 3 6
[4] Wang X F and Chen G R 2002 IEEE Trans. Circuits Syst. I 49 54
[5] Zhou J, Lu J A and Lv J H 2006 IEEE Trans. Autom. Control 51 652
[6] Zhou J, Xiang L and Liu Z R 2007 Physica A 385 729
[7] Zhang G D, Shen Y and Wang L M 2013 Neural Netw. 46 1
[8] Cai S M, Hao J J, He Q B and Liu Z R 2010 Phys. Lett. A 374 2539
[9] Wu J S, Jiao L C and Chen G R 2011 Chin. Phys. B 20 060503
[10] Wang S G and Yao H X 2012 Chin. Phys. B 21 050508
[11] Wang J A 2012 Acta. Phys. Sin. 61 020509 (in Chinese)
[12] Wang J Y, Zhang H G, Wang Z S and Liang H J 2013 Chin. Phys. B 22 090504
[13] Zhang G D and Shen Y 2013 IEEE Trans. Neural Netw. Learn. Syst. 24 1701
[14] Yang X S and Cao J D 2012 IEEE Trans. Neural Netw. Learn. Syst. 23 60
[15] Wang Z S and Zhang H G 2013 Neurocomputing 108 84
[16] Jeong S C, Ji D H, Park J H and Won S C 2013 Nonlinear Dyn. 71 223
[17] Li X, Wang X F and Chen G R 2004 IEEE Trans. Circ. Syst. I 51 2074
[18] Chen T P, Liu X W and Lu W L 2007 IEEE Trans. Circ. Syst. I 54 1317
[19] Zhou J, Lu J A and Lü J H 2008 Automatica 44 996
[20] Yu W W and Chen G R 2009 Automatica 45 429
[21] Song Q and Cao J D 2010 IEEE Trans. Circ. Syst. I 57 672
[22] Wu Y Q, Li C P, Yang A L, Song L J and Wu Y J 2012 Appl. Math. Comput. 218 7445
[23] Fridman E and Seuret A 2004 Automatica 40 1441
[24] Fridman E 2010 Automatica 46 421
[25] Lu J G and Hill D.J 2008 IEEE Trans. Circ. Syst. II 55 586
[26] Zhang C K, He Y and Wu M 2010 Neurocomputing 74 265
[27] Wu Z G, Shi P, Su H Y and Chu J 2012 IEEE Trans. Neural Netw. Learn. Syst. 23 1368
[28] Li N, Zhang Y L, Hu J W and Nie Z Y 2011 Neurocomputing 74 805
[29] Wu Z G, Park J, Su H Y, Song B and Chu J 2012 J. Franklin Institute 349 2735
[30] Yang X S, Cao J D and Lu J Q 2013 Int. J. Robust. Nonlinear Control 23 2060
[31] Gu K Q 2000 Proceedings of 39th IEEE Conference on Decision and Control 2805
[32] Huang C, Ho D W C and Lu J Q 2012 20th International Symposium on Mathematical Theory of Networks and Systems
[33] Arnold L 1974 Stochastic Differential Equations: Theory and Applications (New York: Wiley)
[1] Analysis of cut vertex in the control of complex networks
Jie Zhou(周洁), Cheng Yuan(袁诚), Zu-Yu Qian(钱祖燏), Bing-Hong Wang(汪秉宏), and Sen Nie(聂森). Chin. Phys. B, 2023, 32(2): 028902.
[2] Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding
Pengli Lu(卢鹏丽) and Wei Chen(陈玮). Chin. Phys. B, 2023, 32(1): 018903.
[3] Effect of observation time on source identification of diffusion in complex networks
Chaoyi Shi(史朝义), Qi Zhang(张琦), and Tianguang Chu(楚天广). Chin. Phys. B, 2022, 31(7): 070203.
[4] An extended improved global structure model for influential node identification in complex networks
Jing-Cheng Zhu(朱敬成) and Lun-Wen Wang(王伦文). Chin. Phys. B, 2022, 31(6): 068904.
[5] 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.
[6] Explosive synchronization: From synthetic to real-world networks
Atiyeh Bayani, Sajad Jafari, and Hamed Azarnoush. Chin. Phys. B, 2022, 31(2): 020504.
[7] Robust H state estimation for a class of complex networks with dynamic event-triggered scheme against hybrid attacks
Yahan Deng(邓雅瀚), Zhongkai Mo(莫中凯), and Hongqian Lu(陆宏谦). Chin. Phys. B, 2022, 31(2): 020503.
[8] Finite-time synchronization of uncertain fractional-order multi-weighted complex networks with external disturbances via adaptive quantized control
Hongwei Zhang(张红伟), Ran Cheng(程然), and Dawei Ding(丁大为). Chin. Phys. B, 2022, 31(10): 100504.
[9] Explosive synchronization in a mobile network in the presence of a positive feedback mechanism
Dong-Jie Qian(钱冬杰). Chin. Phys. B, 2022, 31(1): 010503.
[10] LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks
Gui-Qiong Xu(徐桂琼), Lei Meng(孟蕾), Deng-Qin Tu(涂登琴), and Ping-Le Yang(杨平乐). Chin. Phys. B, 2021, 30(8): 088901.
[11] Complex network perspective on modelling chaotic systems via machine learning
Tong-Feng Weng(翁同峰), Xin-Xin Cao(曹欣欣), and Hui-Jie Yang(杨会杰). Chin. Phys. B, 2021, 30(6): 060506.
[12] Dynamical robustness of networks based on betweenness against multi-node attack
Zi-Wei Yuan(袁紫薇), Chang-Chun Lv(吕长春), Shu-Bin Si(司书宾), and Dong-Li Duan(段东立). Chin. Phys. B, 2021, 30(5): 050501.
[13] Exploring individuals' effective preventive measures against epidemics through reinforcement learning
Ya-Peng Cui(崔亚鹏), Shun-Jiang Ni (倪顺江), and Shi-Fei Shen(申世飞). Chin. Phys. B, 2021, 30(4): 048901.
[14] Improving robustness of complex networks by a new capacity allocation strategy
Jun Liu(刘军). Chin. Phys. B, 2021, 30(1): 016401.
[15] Manufacturing enterprise collaboration network: An empirical research and evolutionary model
Ji-Wei Hu(胡辑伟), Song Gao(高松), Jun-Wei Yan(严俊伟), Ping Lou(娄平), Yong Yin(尹勇). Chin. Phys. B, 2020, 29(8): 088901.
No Suggested Reading articles found!