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Anti-synchronization of stochastic perturbed delayed chaotic neural networks

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Abstract

This paper studies the anti-synchronization of a class of stochastic perturbed chaotic delayed neural networks. By employing the Lyapunov functional method combined with the stochastic analysis as well as the feedback control technique, several sufficient conditions are established that guarantee the mean square exponential anti-synchronization of two identical delayed networks with stochastic disturbances. These sufficient conditions, which are expressed in terms of linear matrix inequalities (LMIs), can be solved efficiently by the LMI toolbox in Matlab. Two numerical examples are exploited to demonstrate the feasibility and applicability of the proposed synchronization approaches.

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Acknowledgments

This work was jointly supported by the National Natural Science Foundation of China under Grant No. 60874088, the Specialized Research Fund for the Doctoral Program of Higher Education under Grant 20070286003 and the Foundation for Excellent Doctoral Dissertation of Southeast University YBJJ0705.

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Authors and Affiliations

  1. Department of Mathematics, Southeast University, 210096, Nanjing, China

    Fengli Ren & Jinde Cao

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  1. Fengli Ren
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  2. Jinde Cao
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Correspondence to Fengli Ren.

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Ren, F., Cao, J. Anti-synchronization of stochastic perturbed delayed chaotic neural networks. Neural Comput & Applic 18, 515–521 (2009). https://doi.org/10.1007/s00521-009-0251-5

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  • DOI: https://doi.org/10.1007/s00521-009-0251-5

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