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Exponential stability of inertial BAM neural networks with time-varying delay via periodically intermittent control

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Abstract

In this paper, we study global exponential stability problem for inertial BAM neural networks with time-varying delay via periodically intermittent control. By utilizing suitable variable substitution, the second-order system can be transformed into first-order differential equations. It is shown that the states of the inertial BAM neural networks with time-varying delay via periodically intermittent control can be globally exponential stabilized with a desired oribis under the designed intermittent controller. Finally, a typical example is chosen to illustrate the validation of the theoretical results.

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Acknowledgments

This publication was made possible by NPRP Grant \(\sharp \) NPRP 4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. This work was also supported by Natural Science Foundation of China (Grant Nos: 61374078, 61403313).

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, People’s Republic of China

    Wei Zhang & Chuandong Li

  2. Department of Mathematics, Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar

    Tingwen Huang

  3. College of Mathematics and Physics, Chongqing University of Science and Technology, Chongqing, 401331, People’s Republic of China

    Jie Tan

Authors
  1. Wei Zhang
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  2. Chuandong Li
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  3. Tingwen Huang
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  4. Jie Tan
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Correspondence to Chuandong Li.

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Zhang, W., Li, C., Huang, T. et al. Exponential stability of inertial BAM neural networks with time-varying delay via periodically intermittent control. Neural Comput & Applic 26, 1781–1787 (2015). https://doi.org/10.1007/s00521-015-1838-7

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  • DOI: https://doi.org/10.1007/s00521-015-1838-7

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