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

被引：0
作者：
Dan He
Bin Zhou
Zhengqiu Zhang
机构：
[1] Hunan Institute of Technology School of Mathematics,College of Mathematics and Econometrics
[2] Physics and Energy Engineering,undefined
[3] Hunan University,undefined
来源：
Neural Processing Letters | 2020年 / 51卷
关键词：
Periodic solutions; A class of neutral-type neural networks with time delays; Combining Mawhin’s continuation theorem of coincidence degree theory with graph theory as well as Lyapunov sequence method;
D O I：
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学科分类号：
摘要：
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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页码：543 / 557
页数：14
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