The Scientific World Journal

Volume 2014 (2014), Article ID 785706, 20 pages

http://dx.doi.org/10.1155/2014/785706

## Cluster Synchronization for a Class of Neutral Complex Dynamical Networks with Markovian Switching

Department of Auto, School of Information Science and Technology, University of Science and Technology of China, Anhui 230027, China

Received 29 August 2013; Accepted 21 November 2013; Published 27 April 2014

Academic Editors: L. Acedo, M. Bruzón, and J. S. Cánovas

Copyright © 2014 Xinghua Liu. 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

cluster synchronization problem for a class of neutral complex dynamical networks (NCDNs) with Markovian switching is investigated in this paper. Both the retarded and neutral delays are considered to be interval mode dependent and time varying. The concept of cluster synchronization is proposed to quantify the attenuation level of synchronization error dynamics against the exogenous disturbance of the NCDNs. Based on a novel Lyapunov functional, by employing some integral inequalities and the nature of convex combination, mode delay-range-dependent cluster synchronization criteria are derived in the form of linear matrix inequalities which depend not only on the disturbance attenuation but also on the initial values of the NCDNs. Finally, numerical examples are given to demonstrate the feasibility and effectiveness of the proposed theoretical results.

#### 1. Introduction

During the past decades, the research on the complex dynamical networks (CDNs) has attracted extensive attention of scientific and engineering researchers in all fields domestic and overseas since the pioneering work of Watts and Strogatz [1]. One of the reasons is that the complex networks have extensively existed in many practical applications, such as ecosystems, the Internet, scientific citation web, biological neural networks, and large scale robotic system; see, for example, [2–4]. It should be noted that the synchronization phenomena of CDNs have been paid more attention to and intensively have been investigated in various different fields; please refer to [5–10] and references therein for more details.

Since time delay inevitably exists and has become an important issue in studying the CDNs, synchronization problems for complex networks with time delays have gained increasing research attention and considerable progress has been made; see, for example, [5–16] and references therein for more details. However, in some practical applications, past change rate of the state variables affects the dynamics of nodes in the networks. This kind of complex dynamical network is termed as neutral complex dynamical network (NCDN), which contains delays both in its states and in the derivatives of its states. There are some results about the synchronization design problem for neutral systems [17–21]. In these works, [18, 19] had studied the synchronization control for a kind of master-response setup and further extended to the case of neutral-type neural networks with stochastic perturbation. References [17, 20] had researched the synchronization problem for a class of complex networks with neutral-type coupling delays. Reference [21] had investigated the robust global exponential synchronization problem for an array of neutral-type neural networks. However, much fewer results have been proposed for neutral complex dynamical networks (NCDNs) compared with the rich results for CDNs with only discrete delays.

Recently, as a special synchronization on CDNs, cluster synchronization has been observed in biological science, distributed computation, and social contact networks. Because most of these networks have the clustering characteristic, many individuals maintain close contact with others in a same cluster, while only a few individuals link with an outside cluster. Hence, the individuals are synchronized inside the same cluster, but there is no synchronization among the clusters. Many researchers have made a lot of progress on the cluster synchronization problem; see, for example, [22–26]. In [24], cluster synchronization criteria are proposed for the coupled Josephson equation by constructing different coupling schemes. Then, in [26], a coupling scheme with cooperative and competitive weigh couplings is used to realize cluster synchronization for connected chaotic networks. In [22], cluster synchronization in an array of hybrid coupled neural networks with delays has been investigated and a new method is proposed to realize cluster synchronization by constructing a special coupling matrix. Besides, in the latest two years, cluster synchronization is considered for an array of coupled stochastic delayed neural networks by using the pinning control strategy in [23]. Linear pinning control schemes are given for cluster mixed synchronization of complex networks with community structure and nonidentical nodes in [25]. However, most of the research results in general complex networks ensure global or asymptotical synchronization, but the external disturbance is always existent, which may cause complex networks to diverge or oscillate. Therefore it is imperative to enhance the anti-interference ability of the system. To our knowledge, not much has been done for cluster synchronization for continuous-time complex dynamical networks with neutral time delays and Markovian switching. The purpose of this paper is to minimize this gap. In addition, due to the complexity of high-order and large-scale networks, network mode switching is also a universal phenomenon in CDNs of the actual systems, and sometimes the network has finite modes that switch from one to another with certain transition rate; then such switching can be governed by a Markovian chain. The stability and synchronization problem of complex networks and neural networks with Markovian jump parameters and delays are investigated in [15, 27–30] and references therein. Motivated by the above analysis, the cluster synchronization problem for a class of NCDNs with Markovian switching and mode-dependent time-varying delays is investigated in this paper. The addressed NCDNs consist of modes and the networks switch from one mode to another according to a Markovian chain.

In this paper, cluster synchronization of the NCDNs with Markovian jump parameters is studied for the first time, which is first introduced to quantify the attenuation level of synchronization error dynamics against the exogenous disturbance of NCDNs with Markovian switching. It is assumed that the neutral and retarded delays are interval mode dependent and time varying. By utilizing a new augmented Lyapunov functional, cluster synchronization criteria, which depend on interval mode-dependent delays, disturbance attenuation lever, and the initial values of NCDNs, are derived based on the Lyapunov stability theory, integral matrix inequalities, and convex combination. All the proposed results are in terms of LMIs that can be solved numerically, which are proved to be effective in numerical examples.

The remainder of the paper is organized as follows. Section 2 presents the problem and preliminaries. Section 3 gives the main results, which are then verified by numerical examples in Section 4. The paper is concluded in Section 5.

*Notations*. The following notations are used throughout the paper. denotes the dimensional Euclidean space and is the set of all matrices. (), where and are both symmetric matrices, meaning that is negative (positive) definite. is the identity matrix with proper dimensions. For a symmetric block matrix, we use to denote the terms introduced by symmetry. stands for the mathematical expectation, is the Euclidean norm of vector , and , while is spectral norm of matrix and . is the eigenvalue of matrix with maximum (minimum) real part. The Kronecker product of matrices and is a matrix in which is denoted by . Let and denotes the family of continuous function , from to with the norm . Matrices, if their dimensions are not explicitly stated, are assumed to have compatible dimensions for algebraic operations.

#### 2. Problem Statement and Preliminaries

Given a complete probability space where is the sample space, is the algebra of events and is the probability measure defined on . Let be a homogeneous and right-continuous Markov chain taking values in a finite state space with a generator , , which is given by where , , (, ) is the transition rate from mode to and, for any state or mode , it satisfies Moreover, it is assumed that is irreducible and available at time .

The following neutral complex dynamical network (NCDN) consisting of identical nodes with Markovian jump parameters and interval time-varying delays over the space is investigated in this paper: where and are state variable and the controlled output of the node , respectively. is the exogenous disturbance input. describes the evolution of the mode. , , , , , and represent the connection weight matrices and the delayed connection weight matrices with real values in mode . () is the disturbance matrix in mode . is a parametric matrix in mode . In this paper, these parametric matrices of NCDN (3) and (4) are known constant matrices in certain mode . , , are continuously nonlinear vector functions which are with respect to the current state , the delayed state , and the neutral delay state . , , and represent the inner-coupling matrices linking between the subsystems in mode . , , and are the coupling configuration matrices of the networks representing the coupling strength and the topological structure of the NCDNs in mode , in which is defined as follows. If there exists a connection between th and th () nodes, then ; otherwise and and denote the mode-dependent time-varying neutral delay and retarded delay, respectively. They are assumed to satisfy where , , , and are real constant scalars and .

The nonlinear vector functions, , , and , are assumed to satisfy the following sector-bounded condition [31]: where and , , are two constant matrices with . Such a description of nonlinear functions has been exploited in [32–34] and is more general than the commonly used Lipschitz conditions, which would be possible to reduce the conservatism of the main results caused by quantifying the nonlinear functions via a matrix inequality technique.

For simplicity of notations, we denote , , , , , , , , (), , and by , , , , , , , , (), , and for . By utilizing the Kronecker product of the matrices, (3) and (4) can be written in a more compact form as where

*Assumption 1 (see [22]). *The coupling matrix can be expressed in the following form:
It should be especially emphasized that we do not assume that the coupling matrix is symmetric or diagonal. However, most of the former works about network synchronization are based on symmetric or diagonal coupling matrix.

Before moving onto the main results, some definitions and lemmas are introduced below.

*Definition 2 (see [35]). *Define operator by . is said to be stable if the homogeneous difference equation
is uniformly asymptotically stable. In this paper, that is, .

*Definition 3 (see [36]). *Define the stochastic Lyapunov-Krasovskii function of the NCDNs (3) and (4) as where its infinitesimal generator is defined as

*Definition 4 (see [26]). *A network with nodes realizes cluster synchronization if the nodes are split into several clusters, such as , , , , and the nodes in the same cluster synchronize with one another (i.e., for the states and of arbitrary nodes and in the same cluster, holds). The set
is called the cluster synchronization manifold.

Lemma 5 (see [37]). *Let be an matrix in the set , where denotes a ring and the set of matrices with entries such that the sum of the entries in each row is equal to for some . Then the matrix satisfies , where ,
**
Furthermore, the matrix can be rewritten explicitly as follows:
*

*Lemma 6. Under Assumption 1, the matrix satisfies , , where
And , , , , and .*

*Proof. *From Assumption 1 and Lemma 5, it can be easily obtained that
This completes the proof.

*Lemma 7 (see [22]). if and only if , , where .*

*Proof. *Consider
By Definition 4, it completes the proof.

*Definition 8. *The neutral complex dynamical networks (3) and (4) are cluster synchronization with a disturbance attenuation and symmetric positive matrix , if the following condition is satisfied:
The index is called disturbance attenuation and used to quantify the attenuation level of synchronization error dynamics against exogenous disturbances. It is noticed that (20) depends not only on the attenuation level but also on the initial values of complex networks.

*Lemma 9 (see [38]). Given matrices , , , and with appropriate dimensions and scalar , by the definition of the Kronecker product, the following properties hold:
*

*Lemma 10 (see [39, 40]). For any constant matrix and scalars such that the following integrations are well defined, then(a)(b)*

*Lemma 11 (see [41]). Supposing that , , , and are constant matrices of appropriate dimensions, then
if and only if and hold.*

*3. Main Results*

*3. Main Results*

*In this section, sufficient conditions are presented to ensure cluster synchronization for the neutral complex dynamical network (NCDN) (3) and (4).*

*3.1. Cluster Synchronization Analysis*

*3.1. Cluster Synchronization Analysis*

*Theorem 12. Given the transition rate matrix , the initial positive definite matrix , constant scalars , , , , , and , satisfying , , respectively, the NCDN systems (3) and (4) with sector-bounded condition (7) are cluster synchronization with a disturbance attenuation lever if and there exist symmetric positive matrices , (), , (), , (), , , and , () for any scalars such that the following linear matrix inequalities hold:
where
where are block entry matrices; that is, is a linear operator on real square matrices by
*

*Proof. *Construct the Lyapunov functional candidate as follows:
where
By the structure of and by Lemmas 6 and 9, we obtain the following equalities:
Taking as its infinitesimal generator along the trajectory of (8), we obtain the following from Definition 3 and (30)–(32):