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Asymptotic and Robust Mean Square Stability Analysis of Impulsive High-Order BAM Neural Networks with Time-Varying Delays

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Abstract

In this paper, the globally asymptotical stability in the mean square for a class of high-order bidirectional associative memory neural networks with time-varying delays and fixed moments of impulsive effect are studied. The proof makes use of Lyapunov–Krasovskii functionals, and the conditions are expressed in terms of linear matrix inequalities. A controller has been derived to robustly stabilize this network. Two illustrative examples are also given at the end of this paper to show the effectiveness of our results.

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Acknowledgements

The authors would like to thank the Associate Editor and the anonymous Reviewers for their constructive comments and suggestions to improve the quality of the paper. This work was supported financially by the Natural Science Foundation of Shandong Province under Grant No. ZR2017MA045; the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China, under Grant No. ICT170289.

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  1. School of Mathematical Sciences, Qufu Normal University, Qufu, 273165, Shandong, China

    Yingxin Guo & Luyao Xin

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  1. Yingxin Guo
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  2. Luyao Xin
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Correspondence to Yingxin Guo.

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Guo, Y., Xin, L. Asymptotic and Robust Mean Square Stability Analysis of Impulsive High-Order BAM Neural Networks with Time-Varying Delays. Circuits Syst Signal Process 37, 2805–2823 (2018). https://doi.org/10.1007/s00034-017-0706-3

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  • DOI: https://doi.org/10.1007/s00034-017-0706-3

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