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Exponential synchronization of memristive Cohen–Grossberg neural networks with mixed delays

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Abstract

This paper concerns the problem of global exponential synchronization for a class of memristor-based Cohen–Grossberg neural networks with time-varying discrete delays and unbounded distributed delays. The drive-response set is discussed. A novel controller is designed such that the response (slave) system can be controlled to synchronize with the drive (master) system. Through a nonlinear transformation, we get an alternative system from the considered memristor-based Cohen–Grossberg neural networks. By investigating the global exponential synchronization of the alternative system, we obtain the corresponding synchronization criteria of the considered memristor-based Cohen–Grossberg neural networks. Moreover, the conditions established in this paper are easy to be verified and improve the conditions derived in most of existing papers concerning stability and synchronization for memristor-based neural networks. Numerical simulations are given to show the effectiveness of the theoretical results.

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Acknowledgments

This work was jointly supported by the National Natural Science Foundation of China (NSFC) under Grants Nos. 61263020, 11101053, 61322302, 61104145, 61272530, and 11072059, the Scientific Research Fund of Chongqing Normal University under Grants Nos. 12XLB031 and No. 940115, the Scientific Research Fund of Chongqing Municipal Education Commission under Grant No. KJ130613, and the Program of Chongqing Innovation Team Project in University under Grant No. KJTD201308, the Natural Science Foundation of Jiangsu Province of China under Grant BK2012741, the Specialized Research Fund for the Doctoral Program of Higher Education under Grants 20110092110017 and 20130092110017.

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Authors and Affiliations

  1. Department of Mathematics, Chongqing Normal University, Chongqing, 401331, China

    Xinsong Yang

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

    Jinde Cao & Wenwu Yu

  3. Department of Mathematics, Faculty of Science, King Abdulaziz University, Jidda, 21589, Saudi Arabia

    Jinde Cao

  4. School of Electrical and Computer Engineering, RMIT University, Melbourne, VIC, 3001, Australia

    Wenwu Yu

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  1. Xinsong Yang
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  2. Jinde Cao
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  3. Wenwu Yu
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Correspondence to Xinsong Yang.

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Yang, X., Cao, J. & Yu, W. Exponential synchronization of memristive Cohen–Grossberg neural networks with mixed delays. Cogn Neurodyn 8, 239–249 (2014). https://doi.org/10.1007/s11571-013-9277-6

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  • DOI: https://doi.org/10.1007/s11571-013-9277-6

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