Abstract
This article mainly explores a class of non-autonomous inertial neural networks with time-varying delays and coefficients. By combining Lyapunov function method with differential inequality approach, some novel assertions are gained to guarantee the existence and exponential stability of periodic solutions for the addressed model. An example and its numerical simulations are given to support the proposed approach. The obtained results play an important role in designing the inertial neural networks and complement the earlier publications.
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Acknowledgements
The authors would like to thank the anonymous referees and the editor for very helpful suggestions and comments which led to improvements of our original paper. This work was supported by the National Natural Science Foundation of China (Nos.11861037, 71471020, 51839002), the Hunan Provincial Natural Science Foundation of China(No. 2016JJ1001), the Scientific Research Fund of Hunan Provincial Education Department(No. 15A003) and the Zhejiang Provincial Natural Science Foundation of China (Grant no.LY18A010019).
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Huang, C., Yang, L. & Liu, B. New Results on Periodicity of Non-autonomous Inertial Neural Networks Involving Non-reduced Order Method. Neural Process Lett 50, 595–606 (2019). https://doi.org/10.1007/s11063-019-10055-3
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DOI: https://doi.org/10.1007/s11063-019-10055-3