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Global asymptotic stability of impulsive fractional-order BAM neural networks with time delay

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Abstract

In this paper, we study the global asymptotic stability of fractional-order BAM neural networks. We take both time delay and impulsive effects into consideration. Based on Lyapunov stability theorem, fractional Barbalat’s lemma and Razumikhin-type stability theorem, some stability conditions that are independent of the form of specific delays can be obtained. At last, two illustrative examples are given to show the independence of the obtained two main results and to show the effectiveness of the obtained results.

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Authors and Affiliations

  1. School of Science, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Yongqing Yang, Xianyun Xu & Li Li

  2. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, People’s Republic of China

    Fei Wang, Yongqing Yang & Li Li

Authors
  1. Fei Wang
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  2. Yongqing Yang
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  3. Xianyun Xu
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  4. Li Li
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Corresponding author

Correspondence to Yongqing Yang.

Additional information

This work was jointly supported by the National Natural Science Foundation of China under Grant 11226116, the Fundamental Research Funds for the Central Universities JUSRP51317B.

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Wang, F., Yang, Y., Xu, X. et al. Global asymptotic stability of impulsive fractional-order BAM neural networks with time delay. Neural Comput & Applic 28, 345–352 (2017). https://doi.org/10.1007/s00521-015-2063-0

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  • DOI: https://doi.org/10.1007/s00521-015-2063-0

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