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Exponential stabilization of memristor-based neural networks with unbounded time-varying delays

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成果类型:
期刊论文
作者:
Zhao, Jiemei*
通讯作者:
Zhao, Jiemei
作者机构:
[Zhao, Jiemei] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
通讯机构:
[Zhao, Jiemei] W
Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
语种:
英文
期刊:
中国科学:信息科学(英文版)
ISSN:
1674-733X
年:
2021
卷:
64
期:
8
页码:
1-3
基金类别:
supported by Research and Innovation Initiatives of WHPU (Grant No. 2018Y20);
机构署名:
本校为第一且通讯机构
院系归属:
数学与计算机学院
摘要:
Dear editor, As a consequence of symmetry arguments,the memristor was predicted by Chua[1].As the fourth basic circuit element,its memory characteristic and nanometer dimen-sions are devoid of resistors,capacitors,and inductors.In the field of the dynamical behavior analysis for memristive neural networks(MNNs),information exchange and signal transmission among different neurons are time-varying ac-tivities and discrete time delays are frequently supposed to be bounded,which implies that the current state of a neuron depend only on a part of its history.Actually,the current behavior of a neur...
摘要(中文):
<正>Dear editor,As a consequence of symmetry arguments, the memristor was predicted by Chua [1]. As the fourth basic circuit element, its memory characteristic and nanometer dimensions are devoid of resistors, capacitors, and inductors. In the field of the dynamical behavior analysis for ...

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