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Global synchronization of complex networks with discrete time delays and stochastic disturbances

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Abstract

This paper regards the global synchronization problem for a class of complex networks with both discrete time delays and stochastic disturbances. A sufficient condition that ensures the complex system to be globally synchronized is derived by referring to the Lyapunov functional method and the properties of Kronecker product. By using ‘delay-fractioning’ approach, the upper bounds of time delays of complex networks are greatly enlarged when synchronization achieves. Therefore, the result obtained in this paper is much less conservative than the existing ones. Furthermore, the proposed synchronization criterion, which is expressed in terms of linear matrix inequalities (LMIs), can be efficiently solved employing the Matlab LMI toolbox.

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References

  1. Dangalchev C (2004) Generation models for scale-free networks. Phys A 338:659–671

    Article  MathSciNet  Google Scholar 

  2. Cao J, Ho DWC, Huang X (2007) LMI-based criteria for global robust stability of bidirectional associative memory networks with time delay. Nonlinear Anal 66:1558–1572

    Article  MathSciNet  Google Scholar 

  3. Lu JQ, Cao J (2007) Adaptive synchronization in tree-like dynamical networks. Nonlinear Anal Real World Appl 8:1252–1260

    Article  MathSciNet  Google Scholar 

  4. Liang JL, Cao J, Ho DWC (2005) Discrete-time bidirectional associative memory neural networks with variable delays. Phys Lett A 335:226–234

    Article  Google Scholar 

  5. Strogatz SH (2001) Exploring complex networks. Nature 410:268–276

    Article  Google Scholar 

  6. Cao J, Li P, Wang WW (2006) Global synchronization in arrays of delayed neural networks with constant and delayed coupling. Phys Lett A 353:318–325

    Article  Google Scholar 

  7. Nie XB, Cao J (2009) Stability analysis for the generalized Cohen-Grossberg neural networks with inverse Lipschitz neuron activations. Comput Math Appl 57:1522–1536

    Article  MathSciNet  Google Scholar 

  8. Qiu JL, Cao J (2009) Global synchronization of delay-coupled genetic oscillators. Neurocomputing 72:3845–3850

    Article  Google Scholar 

  9. Yu WW, Cao J (2007) Synchronization control of stochastic delayed neural networks. Phys A 373:252–260

    Article  Google Scholar 

  10. Liang JL, Wang ZD, Liu Y, Liu X (2008) Global synchronization control of general delayed discrete-time networks with stochastic coupling and disturbances. IEEE Trans Syst Man Cybern B 38:1073–1083

    Article  Google Scholar 

  11. Sáchez E, Matias M, Mun̂uzuri V (1997) Analysis of synchronization of chaotic systems by noise: an experimental study. Phys Rev E 56:4068–4071

    Article  Google Scholar 

  12. Wang Y, Wang ZD, Liang JL (2008) A delay fractioning approach to global synchronization of delayed complex networks with stochastic disturbances. Phys Lett A 372:6066–6073

    Article  Google Scholar 

  13. Wang ZD, Liu Y, Li M, Liu X (2006) Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays. IEEE Trans Neural Netw 17:814–820

    Article  Google Scholar 

  14. Langville AN, William WJ, Stewart J (2004) The Kronecker product and stochastic automata networks. J Comput Appl Math 167:429–447

    Article  MathSciNet  Google Scholar 

  15. Wang ZD, Shu HS, Fang JA, Liu XH (2006) Robust stability for stochastic Hopfield neural networks with time delays. Nonlinear Anal Real World Appl 7:1119–1128

    Article  MathSciNet  Google Scholar 

  16. Arnold L (1998) Random dynamical systems. Springer, Berlin

    Google Scholar 

  17. Khasminskii RZ (1980) Stochastic stability of differential equations. Alphen aan den Rijn, Netherlands

    Google Scholar 

  18. Mao X (1994) Exponential stability of stochastic differential equations. Marcel Deker, New York

    Google Scholar 

  19. Mou S, Gao H, Qiang W, Chen K (2008) New delay-dependent exponential stability for neural networks with time delay. IEEE Trans Syst Man Cybern B 38:571–576

    Article  Google Scholar 

  20. Gu KQ, Kharitonov VL, Chen J (2003) Stability of time-delay systems. Birkhauser, Boston

    Book  Google Scholar 

  21. Liu YR, Wang ZD, Liu XH (2008) Exponential synchronization of complex networks with Markovian jump and mixed delays. Phys Lett A 372:3986–3998

    Article  MathSciNet  Google Scholar 

  22. Boyd S, Ghaoui LE, Feron E, Balakrishnan V (1994) Linear matrix inequalities in system and control theory. SIAM, Philadelphia

    Google Scholar 

Download references

Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant 60874088 and the Natural Science Foundation of Jiangsu Province of China under Grant BK2009271.

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Correspondence to Quanxin Cheng.

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Cheng, Q., Cao, J. Global synchronization of complex networks with discrete time delays and stochastic disturbances. Neural Comput & Applic 20, 1167–1179 (2011). https://doi.org/10.1007/s00521-010-0467-4

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  • DOI: https://doi.org/10.1007/s00521-010-0467-4

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