Abstract and Applied Analysis

Volume 2013 (2013), Article ID 167198, 6 pages

http://dx.doi.org/10.1155/2013/167198

Research Article

Synchronization between Two Discrete-Time Networks with Mutual Couplings

Meng Xiao, Weigang Sun, and Fangyue Chen

Institute of Applied Mathematics and Engineering Computations, Hangzhou Dianzi University, Hangzhou 310018, China

Received 22 September 2013; Accepted 4 November 2013

Academic Editor: Jianquan Lu

Copyright © 2013 Meng Xiao 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.

Abstract.

We investigate synchronization between two discrete-time networks with mutual couplings, including inner synchronization inside each network and outer synchronization between two networks. We then obtain a synchronized criterion for the inner synchronization inside each network by the method of linear matrix inequality and derive a relationship between the inner and outer synchronization. Finally, we show numerical examples to verify our theoretical analysis and discuss the effect of coupling strengths, node dynamics, and topological structures on the inner and outer synchronization. Compared to the inner synchronization inside each network, the outer synchronization between two networks is difficult to achieve.

1. Introduction

Network synchronization, as a collective behavior existing inside a network, has been widely studied since the birth of small-world and scale-free networks [1–3]. The main focus is to investigate the interplay between the complexity in the overall topology and the local dynamics of the coupled nodes [4–6]. The topological structures may be globally connected, random, small-world, and scale-free. There are many applications using the synchronization of networks [7], for instance, secure communication and multirobot coordination control. Apart from the complete synchronization appearing inside a network, there are some other types of synchronization, such as phase synchronization, generalized synchronization, lag synchronization, and cluster synchronization [8–12].

Generally, we refer to synchronization happening between two networks as outer synchronization [13], which is distinguished from inner synchronization inside a network. Compared to the inner synchronization, outer synchronization of two networks is more complex, which involves more system parameters. In 2007, Li et al. first proposed the concept of outer synchronization and applied the open-plus-closed-loop method to realize the outer synchronization between two networks with identical topologies [13]. Shortly later, using the adaptive control method, Tang et al. achieved the outer synchronization between two networks with different topological structures [14]. In [15], Wu et al. studied the generalized outer synchronization between two networks with different dimensions of node dynamics. In addition, there are many works on the outer synchronization, that is, introducing the noise, time delay, fractional order node dynamics, and unknown parameters [16–21].

In the above-mentioned works on the outer synchronization, the researchers usually applied the control methods to realize the outer synchronization and did not study the inner synchronization inside a network. In reality, the mutual coupling forms between two networks are colorful; for instance, Wu et al. investigated the outer synchronization between two networks with two active forms nonlinear signals and reciprocity [22]; however, these two coupling forms do not make the outer synchronization happen. In addition, the inner synchronization inside each network was not considered. In [23], Sorrentino and Ott provided a method to study the inner synchronization of two groups. The problem of collective behaviors inside a network and between two networks is of broad interest. For example, in subway systems, when the trains reach the platform, the outer and inner doors simultaneously open or close, showing that both inner and outer synchronization happen [24]. It is also found that present studies on the synchronization between two networks with various couplings are much less, then studying the effect of various couplings on the synchronization is interesting and meaningful.

Inspired by the above discussions, we study synchronization between two discrete-time networks with mutual couplings, including inner synchronization inside each network and outer synchronization between them. By the Lyapunov stability theory and linear matrix inequality, we obtain a synchronous theorem on the inner synchronization inside each network and a relationship between the inner and outer synchronization. Numerical simulations show that the inner synchronization is easier to achieve than the outer synchronization. In addition, given the mutual coupling matrices and appropriate node dynamics, we can adjust coupling strengths to realize the inner and outer synchronization simultaneously. In Section 2, network models and synchronization analysis are presented, and numerical examples are shown in Section 3. Finally, the discussions are included in the last section.

2. Model Presentation and Synchronization Analysis

In this paper, we investigate the synchronization between two discrete-time networks with mutual couplings. The dynamical equations are described as follows:

where the node dynamical equations are and . and are continuously differential functions. is an -dimensional state vector. is the number of network nodes. are the coupling strengths. and represent the mutual coupling matrices between these two networks, whose entries denote the intensity from in network to in network ; analogously, the entries of are the same defined as .

Let us now consider the possibility whether the individual networks achieve inner synchronization; that is, and , where denotes the Euclidean norm of a vector. If there exist such synchronous states, satisfying

without loss of generality, we set .

Thus the synchronized state equations are

Linearizing the synchronous states around and , we obtain

where , , and are the Jacobians of at and , respectively. Assume and let and . Then (4) is rewritten as

Further, let ; then (5) reads

where and , where is an identity matrix of size and denotes the transpose of . Generally, the coupling matrix can be decomposed into , where is the Jordan canonical form with complex eigenvalues and contains the corresponding eigenvectors . Denote ; we obtain

where is a block diagonal matrix; that is,

and is the block corresponding to the multiple eigenvalue of ; that is,

Let and . We can rewrite (7) in a component form as

where .

Firstly, we study the system of (10). Let , where , is and the imaginary unit. Then (10) reads

Construct the Lyapunov function as

Then,

where with . If , that is, these matrices are negative definite, then the zero solution of (10) is asymptotically stable.

Secondly, we study the stability of (11). Let ; then

Choose the Lyapunov function as

Then we obtain

where with

If , then the zero solution of (11) is asymptotically stable. Hence we obtain a synchronized theorem for networks (1).

Theorem 1. Consider network systems (1). Assume the mutual coupling matrices . Let be the eigenvalues of , where . If these matrices , then the networks (1) will achieve inner synchronization inside each network.

Remark 2. Note that Theorem 1 only gives a feasibility of the inner synchronization inside each network. When the inner synchronization inside networks and happens, and the synchronized states for a large time, then the outer synchronization between networks and will be achieved.

3. Numerical Examples

In this section, we will give some examples to illustrate our theoretical results obtained in the previous section. We mainly investigate the effect of coupling strengths, node dynamics, and mutual coupling forms on the inner and outer synchronization. We consider the following coupled discrete-time networks, which are in the form of (1):

where the node equations in (19) are both Henön maps, which have colorful dynamical properties, for instance, and ; it has a periodic solution. Since the sum of each row of mutual matrices is one, for simplicity, we take for . To measure the extent to which inner synchronization is achieved, we introduce two quantities, and . In addition, we denote another quantity for to demonstrate whether outer synchronization happens. Given the values of , and , we first study the effect of coupling strengths and on the inner and outer synchronization. Figure 1 shows that the outer synchronization does not happen when the coupling strength is , while the inner synchronization inside network always appears. In the same way, considering the effect of coupling strength , the details are shown in Figure 2.

Figure 1: The panels exhibit , and at with regard to for and . The bottom one shows that the inner synchronization inside network is easily achieved. When , the inner and outer synchronization simultaneously appear.

Figure 2: The plots show , and at on for and . When , the inner and outer synchronization simultaneously happen.

Next, we discuss the effect of node dynamics on the inner and outer synchronization and take , and and , and . We then investigate the effect of parameter on the inner and outer synchronization. Similarly, given , and and , and , we study the influence of . The numerical simulations are summarized in Figures 3 and 4, showing that the inner synchronization inside network always happens, while the inner synchronization inside network and the outer synchronization only appear for some values of or .

Figure 3: The curves of, and at concerning with , and and , and .

Figure 4: The trajectories of , and at regarding with , and and , and .

Finally, we discuss the effect of network size on the inner and outer synchronization with . Taking the values of , and and and , we plot the curves of , and in Figure 5. In the following, we change the topological structures of mutual coupling matrices and choose as a random matrix; the numerics are shown in Figure 6. It is found that the globally connected and random topological structures have similar effect on the inner and outer synchronization. It is noted that the inner synchronization inside network always happens. A possible reason is the effect of node dynamics. Furthermore, when the Henön map behaves chaotically, no synchronization appears.

Figure 5: The plots show , and at on with , and and and . The topologies are . Obviously the inner synchronization inside network and outer synchronization do not happen for the network size except some values of .

Figure 6: The curves of and at regarding with , and and , and . The coupling matrix is a random matrix satisfying the sum of each row being one.

4. Conclusions

The current study investigated the synchronization between two discrete-time networks with mutual couplings and mainly studied inner synchronization inside each network and outer synchronization between them. We then obtained a synchronous theorem on the inner synchronization inside each network in terms of linear matrix inequality, for the lack of a criterion on the outer synchronization. When the inner synchronization is achieved inside each network and the synchronized states and are same for a large time, then the outer synchronization will happen. From the numerical simulations, we see that the inner and outer synchronization simultaneously happen when we adjust the values of coupling strengths and parameters in the node dynamics. The globally connected and random topologies have similar effect on the inner and outer synchronization. In addition, outer synchronization is more difficult to achieve than the inner synchronization, meaning that the outer synchronization needs a strong coupling form. Because of the diversity of coupling forms between two networks, deriving the criteria on the inner and outer synchronization simultaneously is a technical challenge, which would be discussed in the future.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (nos. 61203155 and 11171084) and Zhejiang Provincial Natural Science Foundation of China under Grant no. LQ12F03003.

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