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Exponential Stability and Sampled-Data Synchronization of Delayed Complex-Valued Memristive Neural Networks

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Abstract

This paper is devoted to the global exponential stability (GES) and synchronization control of delayed complex-valued memristive neural networks (CVMNNs). The criterion on the existence, uniqueness and GES of the equilibrium point (EP) for delayed CVMNNs is established by constructing a suitable Lyapunov functional and using the homeomorphism theory as well as linear matrix inequality. Meanwhile, a sampled-data controller is designed to synchronize the master and slave systems under the framework of inequality techniques and Lyapunov method. Finally, two examples are presented to show the validity of the obtained results.

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Authors and Affiliations

  1. School of Mathematics and Information Science, Shaanxi Normal University, Xi’an, 710062, China

    Huilan Li, Xingbao Gao & Ruoxia Li

Authors
  1. Huilan Li
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  2. Xingbao Gao
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  3. Ruoxia Li
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Corresponding authors

Correspondence to Xingbao Gao or Ruoxia Li.

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This work was supported by National Natural Science Foundation of China (Grant Nos. 61803247 and 61273311), Project Funded by China Postdoctoral Science Foundation 2018M640948, the Fundamental Research Funds for the Central Universities under Grant Nos. GK201903003 and 2017TS002, Shaanxi Postdoctoral Science Foundation under Grant No. 2018BSHEDZZ129, Shaanxi Postdoctoral Science Foundation, Natural Science Basic Research Plan in Shaanxi Province of China No. 2017JQ6063.

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Li, H., Gao, X. & Li, R. Exponential Stability and Sampled-Data Synchronization of Delayed Complex-Valued Memristive Neural Networks. Neural Process Lett 51, 193–209 (2020). https://doi.org/10.1007/s11063-019-10082-0

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