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Global Exponential Synchronization of Memristive Competitive Neural Networks with Time-Varying Delay via Nonlinear Control

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Abstract

This paper investigates the synchronization problem of memristive competitive neural networks (MCNNs) with time-varying delay. Firstly, a novel nonlinear controller with a linear diffusive term and a discontinuous sign function term is introduced. Then, by using this controller, several sufficient conditions for global exponential synchronization of MCNNs are presented based on Lyapunov stability theory and some inequality techniques. Finally, two illustrative examples are provided to substantiate the effectiveness of the obtained theoretical results.

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Correspondence to Zhenyuan Guo.

Additional information

The work described in the paper was supported by Research supported by National Natural Science Foundation of China (61573003) and the Scientific Research Fund of Hunan Provincial Education Department of China (15k026). This publication was made possible by NPRP grant: NPRP 8-274-2-107 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the author[s].

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Gong, S., Yang, S., Guo, Z. et al. Global Exponential Synchronization of Memristive Competitive Neural Networks with Time-Varying Delay via Nonlinear Control. Neural Process Lett 49, 103–119 (2019). https://doi.org/10.1007/s11063-017-9777-1

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