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Global exponential anti-synchronization for delayed memristive neural networks via event-triggering method

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Abstract

This paper studies the exponential anti-synchronization problem of memristive delayed neural networks under the event-triggered controller. To reduce the recalculation of the control signals, two event-triggered control strategies including static and dynamic are proposed. A novel Lyapunov function is constructed to analyze the global exponential anti-synchronization problem. By analysis, we can choose the suitable parameter of the controller to realize global exponential anti-synchronization with a given convergence rate γ without wasting a lot of control resources. Moreover, under event-triggering conditions given in our theorem, we derive that the Zeno behavior will not happen. Finally, numerical examples are given to validate our theorem.

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Acknowledgements

Funding was provided by National Natural Science Foundation of China (Grant No. 61673187).

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Xiaoze Ni & Shiping Wen

  2. School of Mathematics and Econometrics, Hunan University, Changsha, 410082, China

    Yuting Cao & Zhenyuan Guo

  3. Science Program, Texas A&M University at Qatar, Doha, 23874, Qatar

    Tingwen Huang

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  1. Xiaoze Ni
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Correspondence to Shiping Wen.

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Ni, X., Cao, Y., Guo, Z. et al. Global exponential anti-synchronization for delayed memristive neural networks via event-triggering method. Neural Comput & Applic 32, 13521–13535 (2020). https://doi.org/10.1007/s00521-020-04762-5

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