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Novel Sufficient Conditions on Periodic Solutions for Discrete-Time Neutral-Type Neural Networks

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Abstract

In this paper, we consider the existence and global exponential stability of periodic solutions for a class of delayed discrete-time neutral-type neural networks. Novel sufficient conditions to guarantee the existence and global exponential stability of periodic solutions are established for above discrete-time neutral-type neural networks by combining Mawhin’s continuation theorem of coincidence degree theory with graph theory as well as Lyapunov sequence method. Our results on the existence and global exponential stability of periodic solutions are more concise and easily verified than those obtained in Du et al. (J Frankl Inst 353:448–461, 2016).

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Acknowledgements

Funding was provided by Education Department of Hunan Province (Grant No. 201485).

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Authors and Affiliations

  1. Hunan Institute of Technology School of Mathematics, Physics and Energy Engineering, Yang Heng, 421000, Hunan, People’s Republic of China

    Dan He & Bin Zhou

  2. College of Mathematics and Econometrics, Hunan University, Changsha, 410082, People’s Republic of China

    Zhengqiu Zhang

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  1. Dan He
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  2. Bin Zhou
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  3. Zhengqiu Zhang
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Correspondence to Bin Zhou.

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He, D., Zhou, B. & Zhang, Z. Novel Sufficient Conditions on Periodic Solutions for Discrete-Time Neutral-Type Neural Networks. Neural Process Lett 51, 543–557 (2020). https://doi.org/10.1007/s11063-019-10066-0

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