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Mittag–Leffler Stability and Global Asymptotically \(\omega \)-Periodicity of Fractional-Order BAM Neural Networks with Time-Varying Delays

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Abstract

This article studies the Mittag–Leffler stability and global asymptotical \(\omega \)-periodicity for a class of fractional-order bidirectional associative memory (BAM) neural networks with time-varying delays by using Laplace transform, stability theory of fractional systems and some integration technique. Firstly, some sufficient conditions are given to ensure the boundedness and global Mittaag-Leffler stability of fractional-order BAM neural networks with time-varying delays. Next, S-asymptotical \(\omega \)-periodicity and global asymptotical \(\omega \)-periodicity of fractional-order BAM neural networks with time-varying delays are also explored. Finally, some numerical examples and simulation are performed to show the effectiveness of theoretical results.

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Acknowledgements

The work is supported by the National Natural Science Foundation of China (No. 11675112) and Natural Science Foundation of Zhejiang Province (No. LY16A050001). The authors would like to thank the editor and three anonymous reviewers for their valuable suggestions and comments.

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Authors and Affiliations

  1. Department of Mathematics, Shaoxing University, Shaoxing, 312000, Zhejiang, People’s Republic of China

    Fengyan Zhou

  2. College of Civil Engineering, Shaoxing University, Shaoxing, 312000, Zhejiang, People’s Republic of China

    Chengrong Ma

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  1. Fengyan Zhou
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  2. Chengrong Ma
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Correspondence to Fengyan Zhou.

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Zhou, F., Ma, C. Mittag–Leffler Stability and Global Asymptotically \(\omega \)-Periodicity of Fractional-Order BAM Neural Networks with Time-Varying Delays. Neural Process Lett 47, 71–98 (2018). https://doi.org/10.1007/s11063-017-9634-2

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  • DOI: https://doi.org/10.1007/s11063-017-9634-2

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