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STABILITY OF ANTIPERIODIC RECURRENT NEURAL NETWORKS WITH MULTIPROPORTIONAL DELAYS

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成果类型:
期刊论文
作者:
Huang, Chuangxia*;Long, Xin;Cao, Jinde
通讯作者:
Huang, Chuangxia
作者机构:
[Long, Xin; Huang, Chuangxia] Changsha Univ Sci & Technol, Dept Appl Math, Hunan Prov Key Lab Math Modeling & Anal Engn, Changsha 410114, Peoples R China.
[Cao, Jinde] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China.
通讯机构:
[Huang, Chuangxia] C
Changsha Univ Sci & Technol, Dept Appl Math, Hunan Prov Key Lab Math Modeling & Anal Engn, Changsha 410114, Peoples R China.
语种:
英文
关键词:
antiperiodic;proportional delay;recurrent neural network;stability
期刊:
Mathematical Methods in the Applied Sciences
ISSN:
0170-4214
年:
2020
卷:
43
期:
9
页码:
6093-6102
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China [11971076, 51839002]
机构署名:
本校为第一且通讯机构
院系归属:
数学与统计学院
摘要:
In general, a proportional function is obviously not antiperiodic, yet a very interesting fact in this paper shows that it is possible there is an antiperiodic solution for some proportional delayed dynamical systems. We deal with the issue of antiperiodic solutions for RNNs (recurrent neural networks) incorporating multiproportional delays. Employing Lyapunov method, inequality techniques and concise mathematical analysis proof, sufficient criteria on the existence of antiperiodic solutions including its uniqueness and exponential stability are built up. The obtained results provide us some l...

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