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Matrix measure strategies for stabilization and synchronization of delayed BAM neural networks

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Abstract

In this paper, global exponential stabilization and synchronization of a class of bidirectional associative memory (BAM) neural networks with time delays are investigated. Based on the Lyapunov stability theory and matrix measure, we present several sufficient conditions for the global exponential stability of the equilibrium point and several criteria for the global exponentially synchronization. The presented results, which are easy to verify and simple to implement in practice, also provide new insights into the exponential stabilization and synchronization of BAM neural networks. One numerical example is given to illustrate the effectiveness of our theoretical results.

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Acknowledgments

This research is supported by the Natural Science Foundation of China (No. 61374078), Chongqing Research Program of Basic Research and Frontier Technology (No. cstc2015jcyjBX0052), and NPRP Grant # NPRP 4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation).

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, People’s Republic of China

    Yong Li & Chuandong Li

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  1. Yong Li
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  2. Chuandong Li
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Correspondence to Chuandong Li.

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Li, Y., Li, C. Matrix measure strategies for stabilization and synchronization of delayed BAM neural networks. Nonlinear Dyn 84, 1759–1770 (2016). https://doi.org/10.1007/s11071-016-2603-x

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