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Fixed-time synchronization of delayed memristor-based recurrent neural networks

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Abstract

This paper focuses on the fixed-time synchronization control methodology for a class of delayed memristor-based recurrent neural networks. Based on Lyapunov functionals, analytical techniques, and together with novel control algorithms, sufficient conditions are established to achieve fixed-time synchronization of the master and slave memristive systems. Moreover, the settling time of fixed-time synchronization is estimated, which can be adjusted to desired values regardless of the initial conditions. Finally, the corresponding simulation results are included to show the effectiveness of the proposed methodology derived in this paper.

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Acknowledgements

This work was jointly supported by National Natural Science Foundation of China (Grant Nos. 61573096, 61272530), Natural Science Foundation of Jiangsu Province of China (Grant No. BK2012741), and “333 Engineering” Foundation of Jiangsu Province of China (Grant No. BRA2015286), and Scientific Research Foundation of Graduate School of Southeast University (Grant No. YBJJ1663).

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Authors and Affiliations

  1. School of Mathematics, and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing, 210096, China

    Jinde Cao & Ruoxia Li

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  1. Jinde Cao
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  2. Ruoxia Li
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Correspondence to Jinde Cao.

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Cao, J., Li, R. Fixed-time synchronization of delayed memristor-based recurrent neural networks. Sci. China Inf. Sci. 60, 032201 (2017). https://doi.org/10.1007/s11432-016-0555-2

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