Abstract and Applied Analysis

Abstract and Applied Analysis / 2013 / Article
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Coupled Network Systems and Their Collective Behavior

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Research Article | Open Access

Volume 2013 |Article ID 503659 | 5 pages | https://doi.org/10.1155/2013/503659

Projective Lag Synchronization of Delayed Neural Networks Using Intermittent Linear State Feedback

Academic Editor: Jinde Cao
Received12 Aug 2013
Accepted10 Sep 2013
Published18 Nov 2013

Abstract

The problem of projective lag synchronization of coupled neural networks with time delay is investigated. By means of the Lyapunov stability theory, an intermittent controller is designed for achieving projective lag synchronization between two delayed neural networks systems. Numerical simulations on coupled Lu neural systems illustrate the effectiveness of the results.

1. Introduction

In the past few years, synchronization of neural networks has been extensively investigated due to their successful application in many areas, such as communication, modeling brain activity, signal processing, and combinatorial optimization. There are several different synchronization schemes including complete, lag, projective, generalized, phase, and anticipated synchronization [19]. In projective synchronization, the master-slave systems can be synchronized up to a scaling factor. Due to the potential applications in secure communication, projective synchronization has attracted increasing attention [1013]. In [10], the authors study the projective synchronization for different chaotic delayed neural networks via sliding mode control approach. Function projective synchronization of two-cell Quantum-CNN chaotic oscillators using adaptive method is investigated in [11]. It is worth noting that the propagation delay may exist in remote communication systems. However, to the best of the authors’ knowledge, few results (if any) for the projective lag synchronization of neural networks with time delay have been reported in the literature.

In this paper, we will deal with the analysis issue for projective lag synchronization of neural networks with time delay by intermittent control approach. Recently, we have employed this method to stabilize and synchronize chaotic systems [1416]. In this paper, by using Lyapunov stability theory and intermittent control technique, the intermittent controllers and corresponding parameter update rules are designed to obtain projective lag synchronization of neural networks. The rest of the paper is organized as follows. In Section 2, we formulate the problem of projective lag synchronization of coupled neural networks. In Section 3, a general scheme for the projective lag synchronization is presented. Numerical simulations are given in Section 4. Finally, conclusions are given in Section 5.

2. Problem Formulation and Preliminaries

In this paper, we consider the chaotic cellular neural networks described by or, in a compact form, where denotes the state vector, , , and are constant matrices, is the time delay, and are nonlinear functions satisfying the Lipschitz condition, namely; there exist positive constants , such that, for all ,

Consider the corresponding slave system given in the following form: where denotes the state vector, , , and are constant matrices, and denotes the intermittent feedback control defined as follows: where denotes the control strength, denotes the switching rate, denotes the control period, and and are the active control functions.

Let be the transmittal delay. Defining the projective lag synchronization error between systems (2) and (4) as , where denotes projective scaling factor, we have the following error dynamical system:

Under the control of the form (5), the system (6) can be rewritten as

Definition 1. The master system (2) and the slave system (4) are said to be projective lag synchronization if there exist a compact set , and delay time such that, for any initial values , , the error system is exponentially stable; that is,

3. Main Results

This section addresses the projective lag synchronization problem of coupled neural networks.

Theorem 2. Suppose that there exist constants , the coupling strength , time delay , and the functions , such that(i);(ii);(iii);(iv);(v).
Then, the projective lag synchronization error system (7) is globally exponentially stable, that is; the projective lag synchronization between the master system (2) and the slave system (4) under intermittent control (5) is achieved.

Proof. Consider the following Lyapunov function:
When , the derivative of (9) with respect to time along the trajectories of the first subsystem of the system (7) is calculated and estimated as follows: Similarly, when , one obtains Therefore, Then, one observes that By (12) and (13), we can obtain the following.
For ,
For ,
For ,
For , By induction, we have the following.
For , Note that ; in this case, we can obtain
For Note that ; in this case, we can obtain Therefore, for any ,
This implies that the projective lag synchronization error system (7) is globally exponentially stable, and the following estimate holds:
This implies that the projective lag synchronization between the master system (2) and slave system (4) is achieved.

Let and , where , . If we replace the first condition in Theorem 2 with , , then Theorem 2 also can hold. In addition, one can obtain the following corollary from Theorem 2.

Corollary 3. Suppose that there exist positive scalars and satisfying such that where and . Then, the system (7) is exponentially stable, and the projective lag synchronization between the master system (2) and the slave system (4) under intermittent control (5) is achieved.

Remark 4. If , it is clear that the lag synchronization between the system (2) and system (4) will occur.

Remark 5. It is clear that when the time delay vanishes, that is, , we have , which implies that the projective synchronization between master system (2) without delay and system (4) without delay will occur.

Remark 6. From Corollary 3, one observes that the control strength can be estimated as follows:
Note that are determined only by the system itself, and is control parameter. Then, we can estimate the feasible region of control parameters as follows:

4. Numerical Example

In this section, Lu neural oscillator [17] is presented as an example to verify the effectiveness of Theorem 2. The programs DDE23 in MATLAB are used to solve numerically the delay differential equations.

Example 1. Consider the Lu neural oscillator [17] where and .
This model was investigated by Lu in [17] where it was shown to be chaotic, as shown in Figure 1. The corresponding slave system is given by
From Theorem 2, the controller can be obtained as follows: So, when , we have When , we have
Noting that , the feasible region of control parameters is , as shown in Figure 2. For numerical simulation, we select , , , and and plot the norm of projective lag synchronization errors curve, as shown in Figure 3. As the time goes to infinity, the projective lag synchronization error system is stable. Hence, the projective lag synchronization between system (27) and system (29) is achieved.

5. Conclusions

This paper addressed projective lag synchronization of coupled neural networks with time delay. Based on Lyapunov stability theory and adaptive control techniques, several criteria for projective lag synchronization of identical neural networks with time delay have been established. With the proposed method, the simulations of projective lag between coupled Lu systems have showed the effectiveness of theoretical result.

Acknowledgments

This publication was made possible by NPRP Grant no. NPRP 4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. This work was also supported by Natural Science Foundation of China (Grant no. 61374078), the Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant no. KJ121505), the Natural Science Foundation Project of Chongqing CSTC (Grant no. cstc2011jjA40005).

References

  1. L. M. Pecora and T. L. Carroll, “Synchronization in chaotic systems,” Physical Review Letters, vol. 64, no. 8, pp. 821–824, 1990. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
  2. L. M. Pecora and T. L. Carroll, “Driving systems with chaotic signals,” Physical Review A, vol. 44, no. 4, pp. 2374–2383, 1991. View at: Publisher Site | Google Scholar
  3. N. F. Rulkov, M. M. Sushchik, L. S. Tsimring, and H. D. I. Abarbanel, “Generalized synchronization of chaos in directionally coupled chaotic systems,” Physical Review E, vol. 51, no. 2, pp. 980–994, 1995. View at: Publisher Site | Google Scholar
  4. R. Mainieri and J. Rehacek, “Projective synchronization in three-dimensional chaotic systems,” Physical Review Letters, vol. 82, no. 15, pp. 3042–3045, 1999. View at: Publisher Site | Google Scholar
  5. M. G. Rosenblum, A. S. Pikovsky, and J. Kurths, “Phase synchronization of chaotic oscillators,” Physical Review Letters, vol. 76, no. 11, pp. 1804–1807, 1996. View at: Publisher Site | Google Scholar
  6. M. G. Rosenblum, A. S. Pikovsky, and J. Kurths, “From phase to lag synchronization in coupled chaotic oscillators,” Physical Review Letters, vol. 78, no. 22, pp. 4193–4196, 1997. View at: Publisher Site | Google Scholar
  7. C. Masoller and D. H. Zanette, “Anticipated synchronization in coupled chaotic maps with delays,” Physica A, vol. 300, no. 3-4, pp. 359–366, 2001. View at: Publisher Site | Google Scholar | Zentralblatt MATH
  8. X. Li, C. Ding, and Q. Zhu, “Synchronization of stochastic perturbed chaotic neural networks with mixed delays,” Journal of the Franklin Institute, vol. 347, no. 7, pp. 1266–1280, 2010. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
  9. H. Huang, G. Feng, and J. Cao, “Exponential synchronization of chaotic Lur'e systems with delayed feedback control,” Nonlinear Dynamics, vol. 57, no. 3, pp. 441–453, 2009. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
  10. D. Zhang and J. Xu, “Projective synchronization of different chaotic time-delayed neural networks based on integral sliding mode controller,” Applied Mathematics and Computation, vol. 217, no. 1, pp. 164–174, 2010. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
  11. K. S. Sudheer and M. Sabir, “Adaptive function projective synchronization of two-cell Quantum-CNN chaotic oscillators with uncertain parameters,” Physics Letters A, vol. 373, no. 21, pp. 1847–1851, 2009. View at: Publisher Site | Google Scholar | Zentralblatt MATH
  12. S. Chen and J. Cao, “Projective synchronization of neural networks with mixed time-varying delays and parameter mismatch,” Nonlinear Dynamics, vol. 67, no. 2, pp. 1397–1406, 2012. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
  13. L. Chen, Y. Chai, and R. Wu, “Modified function projective synchronization of chaotic neural networks with delays based on observer,” International Journal of Modern Physics C, vol. 22, no. 2, pp. 169–180, 2011. View at: Publisher Site | Google Scholar | Zentralblatt MATH
  14. C. Li, X. Liao, and T. Huang, “Exponential stabilization of chaotic systems with delay by periodically intermittent control,” Chaos, vol. 17, no. 1, Article ID 013103, 2007. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
  15. J. Huang, C. Li, and Q. Han, “Stabilization of delayed chaotic neural networks by periodically intermittent control,” Circuits, Systems, and Signal Processing, vol. 28, no. 4, pp. 567–579, 2009. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
  16. J. Huang, C. Li, T. Huang, and Q. Han, “Lag quasisynchronization of coupled delayed systems with parameter mismatch by periodically intermittent control,” Nonlinear Dynamics, vol. 71, no. 3, pp. 469–478, 2013. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
  17. H. Lu, “Chaotic attractors in delayed neural networks,” Physics Letters A, vol. 298, no. 2-3, pp. 109–116, 2002. View at: Publisher Site | Google Scholar | Zentralblatt MATH

Copyright © 2013 Junjian Huang 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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