Table of Contents Author Guidelines Submit a Manuscript
Discrete Dynamics in Nature and Society
Volume 2011, Article ID 209321, 12 pages
Research Article

Synchronization Analysis of Two Coupled Complex Networks with Time Delays

1School of Science, Hangzhou Dianzi University, Hangzhou 310018, China
2Department of Mathematics, Shanghai University, Shanghai 200444, China

Received 17 August 2010; Revised 10 January 2011; Accepted 31 January 2011

Academic Editor: Recai Kilic

Copyright © 2011 Weigang Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper studies the synchronized motions between two complex networks with time delays, which include individual inner synchronization in each network and outer synchronization between two networks. Based on the Lyapunov stability theory and the linear matrix equality (LMI), a synchronous criterion for inner synchronization inside each network is derived. Numerical examples are given which fit the theoretical analysis. In addition, the involved numerical results show that the delays between two networks have little effect on inner synchronization. It is also shown that synchronous motions within each network or between two networks are not enhanced if individual intranetwork connections are allowed.

1. Introduction

We refer to network synchronization, that is, synchronizing all the nodes inside a network, as “inner synchronization”, which is widely studied in the science world based on the appearance of small-world [1] and scale-free [2] network models. Afterwards, the improved and expanded work in this respect—that is, introducing weighted connections, time varying coupling matrices, nonlinear coupling function, time delays, and so forth—can be found in the literature [310] and many references cited therein. The main approaches on studying synchronization inside a network is decoupling network systems, and studying the low-dimensional systems through the master stability function, or using the tool in equality (LMI toolbox).

Recently “outer synchronization” was proposed, which aimed at studying synchronization between coupled networks. In [11], the authors theoretically and numerically demonstrated the possibility of synchronization between two networks. By the open-plus-closed-loop (OPCL) method [12], synchronization between two networks can be realized with the same topological structures. In reality, if network nodes are of similar properties (same node dynamics), we regard it as one network, otherwise, as more networks. Several examples on two networks are presented in the literature [11, 13].

Actions between two networks are colorful, such as through the nonlinear signals, bipartite connection, or by special nodes. Recently, Sorrentino and Ott studied network synchronization of groups and discussed the feasibility of inner synchronization of individual network [14]. The problem of collective behavior in a network or between two networks is of broad interest. As a first example, in subway systems (e.g., Shanghai, China), when the trains arrive at the platform, the outer and inner doors simultaneously open or close, which shows that both inner synchronization and outer synchronization happen. Another example is two unmanned vehicles are assigned to accomplish independent tasks, such as cooperative searches and attacks [14], which focuses on inner synchronization. If the communicated information between two networks is not amount to lead to desynchronization, we should adopt control skills [1517] to make synchronization happen between them, which has received increasing attention; see [1821]. It is also found that present studies on synchronization inside a network and between two networks simultaneously are much less.

Time delays commonly exist in natural systems, since the finite speed of signal transmission over a distance gives rise to finite time delay, which plays an important role in the stability of synchronization [22]. For the two coupled networks, the signals from one network to another often create delays due to the distance. Therefore, motivated by the impact of topological structures and the delays on the dynamics of the networks, this paper mainly focuses on the effect of delays on inner synchronization inside each network and outer synchronization between two coupled networks. In Section 2, network models and synchronization analysis are presented, and numerical examples are shown in Section 3, including two examples for the same or different dimension of node dynamics. Finally, the discussions are included in the last section.

2. Model Presentation and Synchronization Analysis

Consider the following network models: the node dynamics in network is , , and , , for the nodes in network , where , are continuously differential functions and is an -dimensional (-dimensional) state vector. The dynamical equations of the network systems with time delays are as follows: where is an dimensional coupling matrix, whose entries represent the intensity of the direct interaction from in network to in network , analogously the entries of . Matrix is the inner-coupling matrix. are the time delays between networks. The action sketch between two networks with time delays is shown in Figure 1.

Figure 1: A simple action sketch between two coupled networks with time delays.

For the more general models, we allow connections in each network, the networked system then reads as where , are the coupling matrices, satisfying the sum of every row being zero. , are also inner-coupling matrices.

Let us now consider the possibility that the individual networks achieve synchronization; that is, and . If there exist such synchronous states, satisfying without loss of generality, we assume that . Thus, the synchronized state equations are

Linearizing the synchronous states around and , we get where and are the Jacobians of and at and , respectively.

We assume that the ()-independent solutions of (2.5) can be expressed in the form , , , , where are the suitable time-independent scalars and are the appropriate variables. If the dimension of the space vectors given by the values of    is , we can see that this assumed form includes all possible linear solutions of (2.5). Substituting this assumption into (2.5), they become Thus, in order that (2.6) (resp., (2.7)) is satisfied for all (resp., ), we require that , where is independent of , and , where is independent of . Set and . From this, we get , ; that is,

Substituting (2.8) in (2.6) and (2.7), we obtain One particular solution of (2.8) is derived when , then where is the (possibly complex) eigenvalues of the matrix .

Set , , , , where is a free parameter. Equation (2.10) becomes which shows that the solution of (2.11) contains all the possible solutions of (2.8). We rewrite (2.9) as where .

In [14], the authors gave the explicit analysis on the spectrum of , and two forms of the constructed matrix were shown. Here, we adopt one that matrix has real eigenvalues and only study network (2.1). In the sequel, we utilize the LMI to derive a synchronous theorem.

Theorem 2.1. Consider network model (2.1). If there exist two positive matrices , satisfying where , , then the network (2.1) asymptotically synchronizes to defined by (2.4) for the fixed delays , respectively.

Proof. Consider (2.12). Choose a Lyapunov-Krasovskii functional as Therefore, According to the known condition and Lyapunov-Krasovskii stability Theorem [6], the zero solutions of (2.12) are asymptotically stable, which shows that synchronization inside network and network happens when tends to .

Remark 2.2. Matrix relies upon and . The condition can be changed into when for enough big . The linear matrix equality method is inadequate for assessing the stability of the synchronous solution when both intragroup and extragroup connections are allowed in the network (2.2).

3. Numerical Examples

In this section, we will give two examples to illustrate our obtained results, which includes two cases: and . For the case , we only consider the inner synchronization inside each network. On the other hand, , we discuss the inner synchronization within each network and investigate the outer synchronization between two networks.

Example 3.1. Consider the coupled network dynamical equations [14] below, which are in the form (2.1): where . Here, we take , , , , for numerical simulation. We introduce two quantities and to measure the extent to which synchronization is achieved. The initial values are randomly chosen in .
The intercoupling matrices are
When , , , , and ; by using the Matlab LMI Toolbox, we solve the LMI (2.13) for , , and obtain which indicates that inner synchronization inside each network appears. The inner synchronization evolution curves are depicted in Figure 2. We plots with different values of or in Figure 3, which shows that the delays have little influence on inner synchronization with such intercoupling matrices . Note that the order of magnitude for is .
Next, we change the connection topology of and consider a special case—globally connected, the elements of , , , and with , . The other parameters are the same as in Figure 2. The numerical results are given in Figures 4 and 5. It is shown that the node numbers and connection topology influence the inner synchronization less.

Figure 2: Inner synchronization evolution curves with .
Figure 3: The plot shows at with regard to or , (a) ; (b) .
Figure 4: Inner synchronization evolution curves with globally connected topology, where , , and .
Figure 5: The curves of at versus , with , , and .

Example 3.2. In this example, we let and . Therefore, we discuss inner synchronization inside network or and study synchronization between two networks, that is, outer synchronization proposed in [11], denoting the quantity , to demonstrate that outer synchronization happens. Consider a one-order dynamical system [23] as the dynamical nodes of the complex networks which is described by
The elements of , , , and , when , , , and , by solving the LMI (2.13) for , , we obtain satisfying the conditions of a synchronous criterion, then the individual network achieves its own inner synchronized state. Figure 6 plots the inner and outer synchronization evolution curves. In addition, we plot the curves of quantities with regard to for in Figure 7. We find that the individual network easily achieves inner synchronization for the arbitrary values of , while only when , outer synchronization is realized.
In the following, we numerically discuss the delay effect on inner and outer synchronization. Fix the values of , , , , and , and let the value of vary. We observe that the delay does not influence inner synchronization, and when , outer synchronization disappears. Figure 8 plots the curves of , and with regard to .
Now, we add the individual connections in network and network , and the following network systems are written as: where , are the Laplacian matrices: for all and for all ; obviously, the diffusive couplings , are null in the synchronized manifold. The parameters are chosen as the same as those in Figure 8, and individual couplings are taken as two random matrices. Numerical results show that the adding individual connections have little effect on inner and outer synchronization.

Figure 6: Inner and outer synchronization evolution curves with , , , , , , and .
Figure 7: The curves of , and versus for . The other parameters are , , , and .
Figure 8: The plot show , and versus with , , , , and .

4. Conclusions

In conclusion, we have studied inner synchronization inside networks and and outer synchronization between them with time delays. A synchronous criterion for the occurrence of inner synchronization has been derived. Numerical examples show that the delays have little effect on inner synchronization, how to estimate the domain of delays and is an interesting work. It is noted that in Example 3.2, outer synchronization only happens in the coupling strength , and perhaps the node dynamics in networks and plays a central role, or the bidirectional delayed coupling is weak. Because of the various kinds of actions between two networks, how to derive the criteria on inner and outer synchronization simultaneously is an technical challenging. The theoretical understanding is helpful to study the synchronization between two or more neural networks [24] with appropriate couplings. We hope that such work will appear elsewhere.


This work was supported by the National Natural Science Foundation of China (nos. 10872119, 11005026, and 61003207) and Research Foundation of Hangzhou Dianzi University (no. KYF075610032).


  1. D. J. Watts and S. H. Strogatz, “Collective dynamics of small-world networks,” Nature, vol. 393, no. 6684, pp. 440–442, 1998. View at Google Scholar
  2. A.-L. Barabási and R. Albert, “Emergence of scaling in random networks,” Science, vol. 286, no. 5439, pp. 509–512, 1999. View at Publisher · View at Google Scholar
  3. C. Zhou, A. E. Motter, and J. Kurths, “Universality in the synchronization of weighted random networks,” Physical Review Letters, vol. 96, no. 3, Article ID 034101, 2006. View at Publisher · View at Google Scholar
  4. A. E. Motter, C. S. Zhou, and J. Kurths, “Enhancing complex-network synchronization,” Europhysics Letters, vol. 69, no. 3, pp. 334–340, 2005. View at Publisher · View at Google Scholar
  5. J. Lü, X. Yu, and G. Chen, “Chaos synchronization of general complex dynamical networks,” Physica A, vol. 334, no. 1-2, pp. 281–302, 2004. View at Publisher · View at Google Scholar
  6. Q. Wang, Z. Duan, G. Chen, and Z. Feng, “Synchronization in a class of weighted complex networks with coupling delays,” Physica A, vol. 387, no. 22, pp. 5616–5622, 2008. View at Publisher · View at Google Scholar
  7. C. P. Li, W. Sun, and D. L. Xu, “Synchronization of complex dynamical networks with nonlinear inner-coupling functions and time delays,” Progress of Theoretical Physics, vol. 114, no. 4, pp. 749–761, 2005. View at Google Scholar
  8. S. M. Cai, J. Zhou, L. Xiang, and Z. Liu, “Robust impulsive synchronization of complex delayed dynamical networks,” Physics Letters. Section A, vol. 372, no. 30, pp. 4990–4995, 2008. View at Publisher · View at Google Scholar
  9. W. Kinzel, A. Englert, G. Reents, M. Zigzag, and I. Kanter, “Synchronization of networks of chaotic units with time-delayed couplings,” Physical Review E, vol. 79, no. 5, Article ID 056207, 2009. View at Publisher · View at Google Scholar
  10. Y. Sun and J. Ruan, “Synchronization in coupled time-delayed systems with parameter mismatch and noise perturbation,” Chaos, vol. 19, no. 4, Article ID 043113, 2009. View at Google Scholar
  11. C. P. Li, W. Sun, and J. Kurths, “Synchronization between two coupled complex networks,” Physical Review E, vol. 76, no. 4, Article ID 046204, 2007. View at Publisher · View at Google Scholar
  12. I. Grosu, R. Banerjee, P. K. Roy, and S. K. Dana, “Design of coupling for synchronization of chaotic oscillators,” Physical Review E, vol. 80, no. 1, Article ID 016212, 2009. View at Publisher · View at Google Scholar
  13. W. Sun, R. Wang, W. Wang, and J. Cao, “Analyzing inner and outer synchronization between two coupled discrete-time networks with time delays,” Cognitive Neurodynamics, vol. 4, no. 3, pp. 225–231, 2010. View at Publisher · View at Google Scholar
  14. F. Sorrentino and E. Ott, “Network synchronization of groups,” Physical Review E, vol. 76, no. 5, Article ID 056114, p. 10, 2007. View at Publisher · View at Google Scholar
  15. Z. S. Duan, J. Wang, G. Chen, and L. Huang, “Stability analysis and decentralized control of a class of complex dynamical networks,” Automatica, vol. 44, no. 4, pp. 1028–1035, 2008. View at Google Scholar
  16. F. Li and J. Sun, “Asymptotic stability of a genetic network under impulsive control,” Physics Letters. Section A, vol. 374, no. 31-32, pp. 3177–3184, 2010. View at Publisher · View at Google Scholar
  17. J. Cao and X. Li, “Stability in delayed Cohen-Grossberg neural networks: LMI optimization approach,” Physica D, vol. 212, no. 1-2, pp. 54–65, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  18. H. Tang, L. Chen, J.-A. Lu, and C. K. Tse, “Adaptive synchronization between two complex networks with nonidentical topological structures,” Physica A, vol. 387, no. 22, pp. 5623–5630, 2008. View at Publisher · View at Google Scholar
  19. M. Sun, C.-Y. Zeng, and L.-X. Tian, “Generalized projective synchronization between two complex networks with time-varying coupling delay,” Chinese Physics Letters, vol. 26, no. 1, Article ID 010501, 2009. View at Publisher · View at Google Scholar
  20. S. Zheng, Q. Bi, and G. Cai, “Adaptive projective synchronization in complex networks with time-varying coupling delay,” Physics Letters. Section A, vol. 373, no. 17, pp. 1553–1559, 2009. View at Publisher · View at Google Scholar
  21. X. Wu, W. X. Zheng, and J. Zhou, “Generalized outer synchronization between complex dynamical networks,” Chaos, vol. 19, no. 1, Article ID 013109, p. 9, 2009. View at Publisher · View at Google Scholar
  22. V. Flunkert, S. Yanchuk, T. Dahms, and E. Schöll, “Synchronizing distant nodes: a universal classification of networks,” Physical Review Letters, vol. 105, no. 25, Article ID 254101, 2010. View at Publisher · View at Google Scholar
  23. W.-G. Sun, C.-X. Xu, C.-P. Li, and J.-Q. Fang, “Synchronization and bifurcation of general complex dynamical networks,” Communications in Theoretical Physics, vol. 47, no. 6, pp. 1073–1075, 2007. View at Publisher · View at Google Scholar
  24. J. L. Liang, Z. Wang, Y. Liu, and X. Liu, “Robust synchronization of an array of coupled stochastic discrete-time delayed neural networks,” IEEE Transactions on Neural Networks, vol. 19, no. 11, pp. 1910–1921, 2008. View at Publisher · View at Google Scholar