Mathematical Problems in Engineering

Mathematical Problems in Engineering / 2018 / Article

Research Article | Open Access

Volume 2018 |Article ID 1737685 | https://doi.org/10.1155/2018/1737685

Yuanyuan Li, Jie Zhong, Jianquan Lu, Zhen Wang, Fuad E. Alssadi, "On Robust Synchronization of Drive-Response Boolean Control Networks with Disturbances", Mathematical Problems in Engineering, vol. 2018, Article ID 1737685, 9 pages, 2018. https://doi.org/10.1155/2018/1737685

On Robust Synchronization of Drive-Response Boolean Control Networks with Disturbances

Academic Editor: Jean Jacques Loiseau
Received06 Apr 2018
Revised07 Jun 2018
Accepted05 Jul 2018
Published06 Aug 2018

Abstract

This paper investigates the robust synchronization of drive-response Boolean control networks (BCNs) with disturbances via semi-tensor product of matrices. Firstly, the definition of robust synchronization is presented for the drive-response BCNs with disturbances. Then, based on the algebraic state space representation of drive-response BCNs, the robustly reachable states/sets are presented to investigate robust synchronization of disturbed BCNs. According to the set of robustly reachable states, some necessary and sufficient criteria are obtained for robust synchronization of drive-response BCNs with disturbances under a given state feedback controller. Finally, an illustrative example is presented to demonstrate the obtained theoretical results.

1. Introduction

As an efficient mathematical model of biological systems and genetic regulatory networks (GRNs), Boolean network (BN) has attracted much attention as a qualitative tool for analyzing GRNs. BN theory was firstly proposed by Kauffman in 1969 [1] to model a gene as a binary device for studying the behavior of large, randomly constructed networks of these genes. Thanks to Kauffman’s pioneering work [1], investigations on Boolean networks (BNs) have attracted great attention from biologists and systems scientists. Consequently, many fundamental and excellent results have been established for BNs [2]. In a BN, each node is characterized by two possible values, 1 and 0, and its value (1 or 0) indicates its measured abundance (expressed or unexpressed, active or inactive). Meanwhile, the state evolution of each node on network is determined by a series of Boolean rules. In the past few years, some basic problems of BNs have been investigated in [3], including the fixed points, cycles, the basin of attractors, and the transient time. BNs with additional inputs called Boolean control networks (BCNs) were firstly proposed in [2] and could be used to design and analyze therapeutic intervention strategies. For example, in [4], Huang and Inber established a simple Boolean control network (BCN) to simulate the dynamics of signaling system within capillary endothelia cells, where two external inputs represent growth factors and cell shape (spreading). Recently, the controllability of BCNs has been widely investigated by numerous researchers [59].

Recently, a new matrix product called semi-tensor product (STP) of matrices proposed by Cheng and his colleagues [10, 11] has been successfully used to study Boolean (control) networks and game theory on static games [12, 13], and many excellent results have been obtained [1423]. Its main idea is to convert a logical function into an algebraic function, and, thus, the dynamic of BN can be uniquely converted into a standard discrete algebraic dynamic [11]. However, it should be noted that one main drawback of the algebraic state expression of BNs is its computational complexity. The algebraic state representation converts a BN with state variables and control inputs into a state-control-space of size . Thus, any algorithm based on this approach has an exponential time-complexity. Moreover, many problems like determining fixed points and observability of BCNs have already been proved to be NP-hard. Hence, the computational complexity is intrinsic and also independent of the models adopted to describe BNs.

Since BN is an efficient model to provide general features of living organism and to well illustrate GRNs, the synchronization problem for BNs (or BCNs) has received considerable research interests in the past few years. The researches on synchronization of BNs (or BCNs) can capture lots of useful information on the evolution of biological systems whose corresponding subsystems influence each other. For example, investigations on synchronized BNs are beneficial to better understand synchronization between two coupled lasers [24]. Hence, there are both theoretical and practical importance for studying the synchronization problem of BNs (or BCNs). Recently, some important results corresponding to complete synchronization for two deterministic BNs have been obtained [25, 26].

In the past few years, much attention has been paid on the synchronization phenomenon of collective behavior by large amount of researches [2734]. Aside from synchronization, the ability of disturbances resisting for synchronized networks has also attracted much attention [3538]. In real world, the external disturbances are ubiquitous and may also lead the coupled networks to some unexpected behaviors or even break the phenomenon of synchronization. Hence, it is desirable to investigate the ability of coupled networks to resist external disturbances. In order to deal with this situation, it is practically significant to investigate robust synchronization of dynamical networks, which has gained much research attention [3941]. Gene regulation is an intrinsically noisy process, and it is always subject to intracellular and extracellular disturbances and environment fluctuations [4244]. For example, a cell apoptosis Boolean model is given to study the antiapoptotic pathway under external disturbance inputs, one may be interested in whether there exists a response system that can achieve synchronization with the apoptosis network without disturbances. Thus, when modeling GRNs, disturbances should be considered, as the states of genes may be subject to instantaneous perturbations and experience abrupt disturbances [45, 46]. These disturbance inputs may prohibit the effectiveness of control strategies in keeping the cellular states of biological systems and GRNs in a desirable set. Thus, it is necessary for us to design some control mechanisms under which some coupled GRNs are robustly synchronized to the external disturbances. Unfortunately, concerning BCNs, to the best of our knowledge, there is no result available corresponding to robust synchronization.

In this paper, using STP technique, the robust synchronization problem of drive-response BCNs with disturbances is investigated, and some necessary and sufficient criteria are obtained to check whether a given drive-response BCNs with disturbances can be robustly synchronized. Moreover, a set of robustly reachable states from a given initial state and a union set of robustly reachable states from the initial states set are proposed to check robust synchronization. Finally, one numerical example is presented to illustrate the main results.

The rest of this paper is structured as follows. In Section 2, we present some necessary notations and preliminaries on STP technique. In Section 3, we present our problem formulation and our main results on robust synchronization of drive-response BCNs with disturbances, by resorting to the matrix expression of logical functions and the STP technique. Illustrative example is given to show the validness of our main results in Section 4.

2. Some Preliminaries

In this section, we present some preliminaries on semi-tensor product (STP) and matrix expression of logical functions, which will be used in later analysis. The main tool used in this paper is STP of matrices. Using STP of matrices, a Boolean (control) network can be expressed in its equivalent algebraic representation.

For statement ease, we firstly present some basic notations: , where superscript presents the transpose of a matrix or a vector; with the usual operations (sum +, product , and negation ); let and be the -th column and the columns’ set of identity matrixes . When , we simply use ; let () and () be the -th column (row) and the set of columns (rows) of matrix ; given an matrix , denote by , , the -th block of an matrix ; i.e., ; is called a logical matrix if , which can be simply denoted as . Moreover, here we call the row indexes of columns for matrix . The set of logical matrices is denoted by . Assume that is a real matrix ( denotes the set of real matrices with order ), if all the entries of are positive (nonnegative), that is, () for any , then simply denote ().

Now, we present the definitions of Boolean addition and Boolean product of Boolean matrices. An matrix is called a Boolean matrix if , where denotes the set of all Boolean matrices. Then, we define the Boolean addition below: , . Therefore, for two Boolean matrices , we can obtain the Boolean addition for and : , . Moreover, we can also define the Boolean product of two Boolean matrices and , denoted by with , . Particularly, if , then .

Then, we introduce the STP “” between matrices (and in particular, vectors) as follows [10].

Definition 1 (see [10]). Consider matrix and matrix . The STP of and is defined as follows: , where , denotes the least common multiple of and . Here, denotes the Kronecker product of matrices.

Remark 2. If , then , which is the standard matrix product. Thus, the STP of matrices is a generalization of the standard matrix product providing a new way to multiply two matrices with arbitrary dimensions.

Definition 3 (see [10]). If , then , where is called power-reducing matrix for -valued logical vectors and .

Definition 4 (see [10]). An matrix is called a swap matrix, if it is constructed by the way: label its columns by and similarly label its rows by . Then its element in the position is assigned asWhen , we denote by or .

Identify Boolean variables 1 and 0 with vectors and . That is to say, we consider a Boolean variable as a vector ; thus a Boolean function with variables is equivalent with a map .

Proposition 5 (see [10]). Let be a Boolean function. Then there exists a unique matrix such that , for every , where is called structure matrix of the logical function .

3. Problem Formulation and Main Results

3.1. Problem Formulation

Since disturbance is an important factor when modeling GRNs, here we consider the following drive-response BCNs with disturbances:Here, and represent the -th node and -th node of drive and response BN, respectively, , are Boolean functions, , . In addition, and are controls and disturbance inputs, , . We simply denote by and the states of drive and response BN, respectively. We can observe that the state evolution of drive-response BCNs depends on the following initial states and . Correspondingly, the dynamic of the state feedback control is in the form ofHere, () are Boolean functions.

Now, we present the definition of robust synchronization for drive-response BCNs (2) with disturbances.

Definition 6. Consider the drive-response BCNs (2) with disturbances and the state feedback control (3). Then, system (2) is said to be robustly synchronized if, for any initial states and any disturbance inputs, there exists an integer such that , .

Remark 7. As we can see from system (2), the disturbances are imposed on the states of drive BN, which implies that the states of drive BN will be affected by the values of disturbances (). Thus, if there exists a state feedback control in form of (3) such that the response BN can always synchronize with the drive BN regardless of the disturbances, it will be welcome. The main objective of this paper is to establish robust synchronization criteria for the drive-response BCNs (2) with disturbances.

3.2. Algebraic form of Drive-Response BCNs with Disturbances

In this subsection, we will convert the drive-response BCNs (2) with disturbances and the feedback control (3) into equivalent algebraic forms by applying STP. Using the vector form of Boolean variables and denoting , , , and , by Proposition 5, we can convert systems (2) and (3) into the following algebraic forms:andwhere , , and are called the state transition matrices of drive BN and response BN and the state feedback gain matrix, respectively.

Further, denote , and we can combine (4) into the following equation:where .

Thus, for the drive-response BCNs (4) with disturbances and the feedback control (5), we obtain another equivalent algebraic form as follows:where and are the state transition matrices of system (6) and state feedback gain matrix of system (5).

Remark 8. Defining , we transfer the disturbed BCNs (4) and control system (5) into system (7). It has been proved in [10] that, by defining , we can get a bijective mapping . For example, if , we can derive that and .

3.3. Main Results on Robust Synchronization

In the following subsection, we will investigate the robust synchronization of the drive-response BCNs (2) with disturbances based on the equivalent algebraic form (7).

Consider the drive-response BCNs (2) with disturbances. For a given state feedback control with , one can see from Proposition 5 thatwhere .

Then, in the following sequel, we introduce a set of robustly reachable states from a given initial state at given step.

Definition 9. A state is said to be a reachable state from the initial state at the -th step if there exists a disturbance sequence , such that . Then, for all possible disturbance sequences , we can obtain the set of all possible reachable states, which is called the set of robustly reachable states from the initial state at the -th step, denoted by .

According to (8), suppose that and , and we can obtain the set of robustly reachable states from at the first step, which is . Then, we can further define the union set of robustly reachable states from the initial states set at the -th step as follows:Particularly, when , we denote .

The following proposition gives some important properties for the union set .

Proposition 10. (1) Let be the union set of robustly reachable states from the initial states set at the -th step, and then we have the following relationship:(2) If there exists an integer such that , then we have , .

Proof. Since , we have Suppose that there exists an integer such that ; i.e., . Then we haveThis completes the proof.

By resorting to the above important properties of the union set , one can derive the following result, which will be an auxiliary result for the robust synchronization.

Proposition 11. Assume that is the smallest positive integer such that , and then we have .

Proof. If , then we have , which implies that . Now we claim that . To prove this claim, it is sufficient to prove that holds for every . We draw the conclusion by induction on . Consider the case of . Suppose on the contrary that , which implies that . This implies the fact of , and then we have . This is a contradiction with the fact that ; i.e., . Thus, we prove that, for the case of , .
Now, let and suppose that by induction. According to Proposition 10, we have . This together with the induction hypothesis yields that . If , then we have , which implies that . This contradicts with the fact of the minimality of . Thus, one can derive that . This completes the proof.

Based on Proposition 10, we obtain the following necessary and sufficient condition for robust synchronization of the drive-response BCNs (2) with disturbances.

Theorem 12. Consider the drive-response BCNs (2) with disturbances. Let be the union set of robustly reachable states from the initial states set at the -th step. Then system (2) can be robustly synchronized if and only if there exists an integer satisfying , such thatwhere denotes the synchronized states set.

Proof. Based on Proposition 10, if there exists an integer satisfying , such that , then, given any integer , . Then, according to the definition of set , the states of and reach synchronization after steps. The proof of sufficient part can be directly obtained, which is omitted here. This completes the proof.

Remark 13. If , then the equality yields that . Thus, we use the set to denote the synchronized states set. According to Proposition 10, for the initial states set , we can always calculate the union set of robustly reachable states at given step by recurrence method. Moreover, according to Proposition 11, we can conclude that one only needs to calculate the union set of robustly reachable states for finite steps in order to check robust synchronization.

Consider a set . Note that each state () is a column of the identity matrix , we define the column vector form of the set by Boolean summing up of all the states as follows:Here, the -th elements of are 1, and the rest are 0. Particularly, when , one can derive its corresponding column vector form of , denoted by . Thus, for the union set of robustly reachable states from the initial states , i.e., , we use to denote the corresponding column vector form. Then, we haveSince in (8) is a matrix, we split it into equal blocks as , where (). Then, the following result will provide an algebraic representation for .

Theorem 14. Consider the drive-response BCNs (2) with disturbances. Let be the union set of robustly reachable states from the initial states set at the -th step. Then, we have the following equation for the column vector form :

Proof. We prove the result by induction. Consider the case of . Given an initial state , one can obtain the column vector form of as follows:Thus, the union set of robustly reachable states from the initial states set at the first step is , which proves the case of .
Then, we assume that (16) holds for the case of and consider the case of . Suppose that the reachable states in are , where . Note that the trajectory starting from to can be decomposed into trajectory starting from to some () at the -th step and the trajectory from the possible states to at one step. Thus, one can derive that This together with (17) yieldsOn the other hand, one can obtain thatApplying the induction hypothesis, by (19), we have , which proves the case of . Thus, according to the mathematical induction, we can conclude that (16) holds for any positive integer , which completes the proof.

Let be the column vector form of the synchronized states set . Thus, based on Theorem 14, we can obtain the following result for robust synchronization.

Corollary 15. Consider the drive-response BCNs (2) with disturbances. Let be the column vector form of the synchronized states set . Then system (2) can be robustly synchronized if and only if there exists an integer , such that

4. Illustrative Example

In this section, we present an illustrative example to demonstrate the effectiveness of our obtained results.

Consider the following drive-response BCNs with one disturbance input, and each BN has two nodes:andwhere and are two state variables of drive BN and response BN respectively; are two control inputs and is an external disturbance. Here, we use to substitute the symbol , in order to simplify the logical functions. For example, if (), we have ().

Suppose that the state feedback control is given in the following form:Then, using the vector forms of Boolean variables and denoting , , and , we can obtain the following algebraic forms of the drive-response BCNs (22a) and (22b) with disturbance and the state feedback control (23):where , , and .

Then, denote , and we can obtain the following equivalent algebraic form of the drive-response BCNs (24) with disturbance:where . We present the row indexes of columns in matrix as follows:

Figure 1 shows the row indexes of columns in matrix .

Thus, under the state feedback control , we obtain the following system: , where . A calculation yields Our objective is to check whether the drive-response BCNs (22a) and (22b) with disturbances can be robustly synchronized. As discussed in Remark 13, if () then the equality yields that . Thus, we can obtain the synchronized states set and the unsynchronized states set . For example, if , one can obtain that , since and . A calculation yields Thus, according to Corollary 15, we can obtain thatwhich implies that the drive-response BCNs (22a) and (22b) with disturbance can be robustly synchronized. The set of trajectories starting from the initial states set converging to synchronized states set from time to time can be given as follows: As we can see from the above trajectories, at time , there exist two states and in that belong to the unsynchronized states set . At time , there still exists one state in belonging to . Thus, the drive-response BCNs (22a) and (22b) with disturbance can not be robustly synchronized at time . However, at time , there is only one state in , which belongs to the synchronized states set . Thus, the drive-response BCNs (22a) and (22b) with disturbance is robustly synchronized at time . Moreover, the row indexes of the column vector versus time are plotted in Figure 2. Thus, the validness of Corollary 15 has been well illustrated by this example.

5. Conclusion

In this paper, we have investigated robust synchronization of drive-response BCNs with disturbances. Based on the fundamental properties of STP, we firstly have obtained the algebraic representations of drive-response BCNs with disturbances. By defining a set of robustly reachable states from a given initial state at a given time step, we have obtained the set of all possible reachable states for any given disturbance sequences from the global initial states set after given steps, which is called the union set of robustly reachable states from the global initial states set. Then, based on the defined union sets, several necessary and sufficient criteria have been obtained for robust synchronization of disturbed BCNs. Moreover, the obtained results also have been verified that one only needs to calculate finite steps to check whether the drive-response BCNs with disturbances can be robustly synchronized.

Data Availability

No data were used to support this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

This work was supported by the National Natural Science Foundation of China [Grant nos. 61573102, 11671361], the Natural Science Foundation of Jiangsu Province of China [Grant BK20170019], China Postdoctoral Science Foundation [Grant nos. 2014M560377, 2015T80483], Jiangsu Province Six Talent Peaks Project [Grant no. 2015-ZNDW-002], and the Fundamental Research Funds for the Central Universities.

References

  1. S. A. Kauffman, “Metabolic stability and epigenesis in randomly constructed genetic nets,” Journal of Theoretical Biology, vol. 22, no. 3, pp. 437–467, 1969. View at: Publisher Site | Google Scholar
  2. T. Akutsu, M. Hayashida, W.-K. Ching, and M. K. Ng, “Control of Boolean networks: hardness results and algorithms for tree structured networks,” Journal of Theoretical Biology, vol. 244, no. 4, pp. 670–679, 2007. View at: Publisher Site | Google Scholar | MathSciNet
  3. B. Drossel, T. Mihaljev, and F. Greil, “Number and length of attractors in a critical Kauffman model with connectivity one,” Physical Review Letters, vol. 94, no. 8, Article ID 088701, 2005. View at: Publisher Site | Google Scholar
  4. S. Huang and D. E. Ingber, “Shape-dependent control of cell growth, differentiation, and apoptosis: Switching between attractors in cell regulatory networks,” Experimental Cell Research, vol. 261, no. 1, pp. 91–103, 2000. View at: Publisher Site | Google Scholar
  5. H. Li, L. Xie, and Y. Wang, “On robust control invariance of Boolean control networks,” Automatica, vol. 68, pp. 392–396, 2016. View at: Publisher Site | Google Scholar
  6. J. Lu, J. Zhong, C. Huang, and J. Cao, “On pinning controllability of Boolean control networks,” IEEE Transactions on Automatic Control, vol. 61, no. 6, pp. 1658–1663, 2016. View at: Publisher Site | Google Scholar | MathSciNet
  7. J. Lu, J. Zhong, D. W. Ho, Y. Tang, and J. Cao, “On controllability of delayed Boolean control networks,” SIAM Journal on Control and Optimization, vol. 54, no. 2, pp. 475–494, 2016. View at: Publisher Site | Google Scholar | MathSciNet
  8. R. Liu, J. Lu, Y. Liu, J. Cao, and Z. Wu, “Delayed Feedback Control for Stabilization of Boolean Control Networks With State Delay,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 7, pp. 3283–3288, 2018. View at: Publisher Site | Google Scholar
  9. Y. Li, W. Dou, H. Li, and X. Liu, “Controllability, Reachability, and Stabilizability of Finite Automata: A Controllability Matrix Method,” Mathematical Problems in Engineering, vol. 2018, Article ID 6719319, 6 pages, 2018. View at: Publisher Site | Google Scholar | MathSciNet
  10. D. Cheng, H. Qi, and Z. Li, Analysis and Control of Boolean Network: A Semi-Tensor Product Approach, Communications and Control Engineering Series, Springer, New York, USA, 2011. View at: Publisher Site | MathSciNet
  11. D. Cheng and H. Qi, “A linear representation of dynamics of Boolean networks,” IEEE Transactions on Automatic Control, vol. 55, no. 10, pp. 2251–2258, 2010. View at: Publisher Site | Google Scholar | MathSciNet
  12. X. Ding, H. Li, Q. Yang, Y. Zhou, A. Alsaedi, and F. E. Alsaadi, “Stochastic stability and stabilization of n-person random evolutionary Boolean games,” Applied Mathematics and Computation, vol. 306, pp. 1–12, 2017. View at: Publisher Site | Google Scholar | MathSciNet
  13. P. Guo, H. Zhang, F. E. Alsaadi, and T. Hayat, “Semi-tensor product method to a class of event-triggered control for finite evolutionary networked games,” IET Control Theory & Applications, vol. 11, no. 13, pp. 2140–2145, 2017. View at: Publisher Site | Google Scholar | MathSciNet
  14. Y. Zhao, H. Qi, and D. Cheng, “Input-state incidence matrix of Boolean control networks and its applications,” Systems & Control Letters, vol. 59, no. 12, pp. 767–774, 2010. View at: Publisher Site | Google Scholar
  15. J. Zhong, D. W. Ho, J. Lu, and W. Xu, “Switching-signal-triggered pinning control for output tracking of switched Boolean networks,” IET Control Theory & Applications, vol. 11, no. 13, pp. 2089–2096, 2017. View at: Publisher Site | Google Scholar | MathSciNet
  16. Y. Liu, H. Chen, J. Lu, and B. Wu, “Controllability of probabilistic Boolean control networks based on transition probability matrices,” Automatica, vol. 52, pp. 340–345, 2015. View at: Publisher Site | Google Scholar
  17. Y. Liu, J. Lu, and B. Wu, “Some necessary and sufficient conditions for the output controllability of temporal Boolean control networks,” ESAIM: Control, Optimisation and Calculus of Variations, vol. 20, no. 1, pp. 158–173, 2014. View at: Publisher Site | Google Scholar | MathSciNet
  18. J. Lu, H. Li, Y. Liu, and F. Li, “Survey on semi-tensor product method with its applications in logical networks and other finite-valued systems,” IET Control Theory & Applications, vol. 11, no. 13, pp. 2040–2047, 2017. View at: Publisher Site | Google Scholar | MathSciNet
  19. J. Zhong, D. W. Ho, J. Lu, and W. Xu, “Global robust stability and stabilization of Boolean network with disturbances,” Automatica, vol. 84, pp. 142–148, 2017. View at: Publisher Site | Google Scholar | MathSciNet
  20. J. Zhong, J. Lu, T. Huang, and D. W. C. Ho, “Controllability and Synchronization Analysis of Identical-Hierarchy Mixed-Valued Logical Control Networks,” IEEE Transactions on Cybernetics, vol. 47, no. 11, pp. 3482–3493, 2017. View at: Publisher Site | Google Scholar
  21. M. E. Valcher, “Input/output decoupling of Boolean control networks,” IET Control Theory & Applications, vol. 11, no. 13, pp. 2081–2088, 2017. View at: Publisher Site | Google Scholar | MathSciNet
  22. J. Lu, M. Li, Y. Liu, D. W. Ho, and J. Kurths, “Nonsingularity of Grain-like cascade FSRs via semi-tensor product,” Science China Information Sciences, vol. 61, no. 1, 010204, 12 pages, 2018. View at: Publisher Site | Google Scholar | MathSciNet
  23. M. Li, J. Lu, J. Lou, Y. Liu, and F. E. Alsaadi, “The equivalence issue of two kinds of controllers in Boolean control networks,” Applied Mathematics and Computation, vol. 321, pp. 633–640, 2018. View at: Publisher Site | Google Scholar | MathSciNet
  24. M. Ho, Y. Hung, and I. Jiang, “Stochastic coupling of two random Boolean networks,” Physics Letters A, vol. 344, no. 1, pp. 36–42, 2005. View at: Publisher Site | Google Scholar
  25. R. Li and T. Chu, “Complete synchronization of boolean networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 5, pp. 840–846, 2012. View at: Publisher Site | Google Scholar
  26. Y. Liu, L. Sun, J. Lu, and J. Liang, “Feedback controller design for the synchronization of Boolean control networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 9, pp. 1991–1996, 2016. View at: Publisher Site | Google Scholar | MathSciNet
  27. H. Bao, J. H. Park, and J. Cao, “Exponential synchronization of coupled stochastic memristor-based neural networks with time-varying probabilistic delay coupling and impulsive delay,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 1, pp. 190–201, 2016. View at: Publisher Site | Google Scholar | MathSciNet
  28. T. H. Lee and J. H. Park, “Improved criteria for sampled-data synchronization of chaotic Lur'e systems using two new approaches,” Nonlinear Analysis: Hybrid Systems, vol. 24, pp. 132–145, 2017. View at: Publisher Site | Google Scholar | MathSciNet
  29. S. Xu, J. Lam, and D. W. C. Ho, “A New LMI Condition for Delay-Dependent Asymptotic Stability of Delayed Hopfield Neural Networks,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 53, no. 3, pp. 230–234, 2006. View at: Publisher Site | Google Scholar
  30. W. Zhang, J. Cao, A. Alsaedi, and F. E. Alsaadi, “New Methods of Finite-Time Synchronization for a Class of Fractional-Order Delayed Neural Networks,” Mathematical Problems in Engineering, vol. 2017, Article ID 1804383, 9 pages, 2017. View at: Publisher Site | Google Scholar | MathSciNet
  31. Z. Zhang, H. Shao, Z. Wang, and H. Shen, “Reduced-order observer design for the synchronization of the generalized Lorenz chaotic systems,” Applied Mathematics and Computation, vol. 218, no. 14, pp. 7614–7621, 2012. View at: Publisher Site | Google Scholar | MathSciNet
  32. J. Zhou, C. Sang, X. Li, M. Fang, and Z. Wang, “H∞ consensus for nonlinear stochastic multi-agent systems with time delay,” Applied Mathematics and Computation, vol. 325, pp. 41–58, 2018. View at: Publisher Site | Google Scholar | MathSciNet
  33. K. Liang, M. Dai, H. Shen, J. Wang, Z. Wang, and B. Chen, “L2-L∞ synchronization for singularly perturbed complex networks with semi-Markov jump topology,” Applied Mathematics and Computation, vol. 321, pp. 450–462, 2018. View at: Publisher Site | Google Scholar | MathSciNet
  34. H. Gao, J. Xia, G. Zhuang, Z. Wang, and Q. Sun, “Non-fragile finite-time extended dissipative control for a class of uncertain switched neutral systems,” Complexity, vol. 201, Article ID 6581308, 2017. View at: Google Scholar
  35. L. Li, D. W. C. Ho, J. Cao, and J. Lu, “Pinning cluster synchronization in an array of coupled neural networks under event-based mechanism,” Neural Networks, vol. 76, pp. 1–12, 2016. View at: Publisher Site | Google Scholar
  36. Y. Li, “Impulsive Synchronization of Stochastic Neural Networks via Controlling Partial States,” Neural Processing Letters, vol. 46, no. 1, pp. 59–69, 2017. View at: Publisher Site | Google Scholar
  37. L. Li, Z. Wang, Y. Li, H. Shen, and J. Lu, “Hopf bifurcation analysis of a complex-valued neural network model with discrete and distributed delays,” Applied Mathematics and Computation, vol. 330, pp. 152–169, 2018. View at: Publisher Site | Google Scholar | MathSciNet
  38. Z. Wang, X. Wang, Y. Li, and X. Huang, “Stability and Hopf Bifurcation of Fractional-Order Complex-Valued Single Neuron Model with Time Delay,” International Journal of Bifurcation and Chaos, vol. 27, no. 13, Article ID 1750209, 2017. View at: Publisher Site | Google Scholar | MathSciNet
  39. B.-S. Chen, C.-H. Chiang, and S. K. Nguang, “Robust H∞ synchronization design of nonlinear coupled network via fuzzy interpolation method,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 58, no. 2, pp. 349–362, 2011. View at: Publisher Site | Google Scholar | MathSciNet
  40. J. Lu and D. W. C. Ho, “Globally exponential synchronization and synchronizability for general dynamical networks,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 40, no. 2, pp. 350–361, 2010. View at: Publisher Site | Google Scholar
  41. J. Q. Lu, Z. D. Wang, J. D. Cao, D. W. C. Ho, and J. Kurths, “Pinning impulsive stabilization of nonlinear dynamical networks with time-varying delay,” International Journal of Bifurcation and Chaos, vol. 22, no. 7, Article ID 1250176, 12 pages, 2012. View at: Publisher Site | Google Scholar
  42. J. Paulsson, “Summing up the noise in gene networks,” Nature, vol. 427, no. 6973, pp. 415–418, 2004. View at: Publisher Site | Google Scholar
  43. J. Q. Lu and D. W. C. Ho, “Stabilization of complex dynamical networks with noise disturbance under performance constraint,” Nonlinear Analysis: Real World Applications, vol. 12, no. 4, pp. 1974–1984, 2011. View at: Publisher Site | Google Scholar | MathSciNet
  44. L. Chen, R. Wang, T. Zhou, and K. Aihara, “Noise-induced cooperative behavior in a multicell system,” Bioinformatics, vol. 21, no. 11, pp. 2722–2729, 2005. View at: Publisher Site | Google Scholar
  45. D. Z. Cheng, “Disturbance decoupling of Boolean control networks,” IEEE Transactions on Automatic Control, vol. 56, no. 1, pp. 2–10, 2011. View at: Publisher Site | Google Scholar | MathSciNet
  46. M. Yang, R. Li, and T. Chu, “Controller design for disturbance decoupling of Boolean control networks,” Automatica, vol. 49, no. 1, pp. 273–277, 2013. View at: Publisher Site | Google Scholar

Copyright © 2018 Yuanyuan Li 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.


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