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Finite-time synchronization of delayed memristive neural networks via 1-norm-based analytical approach

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Abstract

By using 1-norm-based analytical approach, this paper considers finite-time (FET) synchronization for memristive neural networks (MNNs) with time-varying delays. New quantized controllers are designed, which can save communication channel and play an important role in synchronizing MNNs. By constructing Lyapunov function, and developing 1-norm-based analytical methods, several conditions are derived to guarantee that the MNNs can be synchronized within a settling time. In addition, the settling time is also presented for the considered MNNs. Some numerical simulations are provided to illustrate the theoretical results.

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Acknowledgements

This work was jointly supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61873213, 61673078, 61633011, 61703346.

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Authors and Affiliations

  1. National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology (Chongqing), College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Wanli Zhang, Shiju Yang, Chuandong Li & Hongfei Li

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  1. Wanli Zhang
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  2. Shiju Yang
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  3. Chuandong Li
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  4. Hongfei Li
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Correspondence to Chuandong Li.

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Zhang, W., Yang, S., Li, C. et al. Finite-time synchronization of delayed memristive neural networks via 1-norm-based analytical approach. Neural Comput & Applic 32, 4951–4960 (2020). https://doi.org/10.1007/s00521-018-3906-2

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  • DOI: https://doi.org/10.1007/s00521-018-3906-2

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