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Dynamic behaviors of memristor-based delayed recurrent networks

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Abstract

This paper investigates the problem of the existence and global exponential stability of the periodic solution of memristor-based delayed network. Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based recurrent networks is established. Several sufficient conditions are obtained, which ensure the existence of periodic solutions and global exponential stability of the memristor-based delayed recurrent networks. These results ensure global exponential stability of memristor-based network in the sense of Filippov solutions. And, it is convenient to estimate the exponential convergence rates of this network by the results. An illustrative example is given to show the effectiveness of the theoretical results.

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Acknowledgments

The work is supported by the Natural Science Foundation of China under Grants 60974021 and 61125303, the 973 Program of China under Grant 2011CB710606, the Fund for Distinguished Young Scholars of Hubei Province under Grant 2010CDA081.

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Authors and Affiliations

  1. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Shiping Wen & Zhigang Zeng

  2. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan, 430074, China

    Shiping Wen & Zhigang Zeng

  3. Texas A&M University at Qatar, Doha, 5825, Qatar

    Tingwen Huang

Authors
  1. Shiping Wen
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  2. Zhigang Zeng
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  3. Tingwen Huang
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Correspondence to Zhigang Zeng.

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Wen, S., Zeng, Z. & Huang, T. Dynamic behaviors of memristor-based delayed recurrent networks. Neural Comput & Applic 23, 815–821 (2013). https://doi.org/10.1007/s00521-012-0998-y

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