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Quasi-uniform stability of Caputo-type fractional-order neural networks with mixed delay

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Abstract

In this paper, a class of Caputo-type fractional-order neural networks with mixed delay is introduced. By employing known inequalities, such as Hölder inequality, Cauchy–Schwartz inequality and Gronwall inequality, sufficient conditions are presented to ensure that such neural network is quasi-uniformly stable. Finally, a numerical example is presented to prove the theoretical results.

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Acknowledgments

The authors are extremely grateful to anonymous reviewers for their careful reading of the manuscript and insightful comments, which help to enrich the content. We would also like to acknowledge the valuable comments and suggestions from the editors, which vastly contributed to improve the presentation of this paper.

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Authors and Affiliations

  1. Department of Applied Mathematics, Yanshan University, Qinhuangdao, 066004, China

    Huaiqin Wu, Xinxin Zhang & Shunhui Xue

  2. School of Electrical Engineering, Yanshan University, Qinhuangdao, 066001, China

    Peifeng Niu

Authors
  1. Huaiqin Wu
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  2. Xinxin Zhang
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  3. Shunhui Xue
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  4. Peifeng Niu
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Corresponding author

Correspondence to Huaiqin Wu.

Additional information

This work was jointly supported by the National Natural Science Foundation of China (61573306), the Postgraduate Innovation Project of Hebei province of China (00302-6370019) and High level talent support project of Hebei province of China (C2015003054).

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Wu, H., Zhang, X., Xue, S. et al. Quasi-uniform stability of Caputo-type fractional-order neural networks with mixed delay. Int. J. Mach. Learn. & Cyber. 8, 1501–1511 (2017). https://doi.org/10.1007/s13042-016-0523-1

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  • DOI: https://doi.org/10.1007/s13042-016-0523-1

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