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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2020

21.08.2019

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

verfasst von: Dan He, Bin Zhou, Zhengqiu Zhang

Erschienen in: Neural Processing Letters | Ausgabe 1/2020

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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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Metadaten
Titel
Novel Sufficient Conditions on Periodic Solutions for Discrete-Time Neutral-Type Neural Networks
verfasst von
Dan He
Bin Zhou
Zhengqiu Zhang
Publikationsdatum
21.08.2019
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-019-10066-0

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