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CLC number: TP183; O175

On-line Access: 2020-03-04

Received: 2019-04-07

Revision Accepted: 2019-05-13

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yang Cao

https://orcid.org/0000-0002-6940-0868

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.2 P.324-339

http://doi.org/10.1631/FITEE.1900181


New results on impulsive type inertial bidirectional associative memory neural networks


Author(s):  Chaouki Aouiti, Mahjouba Ben Rezeg, Yang Cao

Affiliation(s):  Department of Mathematics, the University of Carthage, Zarzouna 7021, Bizerta, Tunisia; more

Corresponding email(s):   chaouki.aouiti@fsb.rnu.tn, mahjouba.benrezek@fsb.rnu.tn, caoyeacy@gmail.com

Key Words:  Inertial neural networks, Anti-periodic solutions, Global exponential stability, Impulsive effect, Time-varying delay, Bidirectional associative memory


Chaouki Aouiti, Mahjouba Ben Rezeg, Yang Cao. New results on impulsive type inertial bidirectional associative memory neural networks[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(2): 324-339.

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author="Chaouki Aouiti, Mahjouba Ben Rezeg, Yang Cao",
journal="Frontiers of Information Technology & Electronic Engineering",
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number="2",
pages="324-339",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900181"
}

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%A Chaouki Aouiti
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%A Yang Cao
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TY - JOUR
T1 - New results on impulsive type inertial bidirectional associative memory neural networks
A1 - Chaouki Aouiti
A1 - Mahjouba Ben Rezeg
A1 - Yang Cao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
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SP - 324
EP - 339
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900181


Abstract: 
This paper is concerned with inertial bidirectional associative memory neural networks with mixed delays and impulsive effects. New and practical conditions are given to study the existence, uniqueness, and global exponential stability of anti-periodic solutions for the suggested system. We use differential inequality techniques to prove our main results. Finally, we give an illustrative example to demonstrate the effectiveness of our new results.

脉冲型惯性双向联想记忆神经网络新解

Chaouki AOUITI1,Mahjouba Ben REZEG1,曹阳2,3
1迦太基大学数学系,突尼斯宾泽特,Zarzouna,7021
2东南大学网络空间安全学院,中国南京,211189
3香港大学机械工程系,中国香港

摘要:探究了具有混合时滞和脉冲效应的惯性双向联想记忆神经网络。该研究为类似系统的反周期解的存在性、唯一性和全局指数稳定性提供了新的实用条件。使用微分不等式技术证明我们的主要结果,并给出一个仿真算例证明新结果的有效性。

关键词:惯性神经网络;反周期解;全局指数稳定;脉冲;时变延迟;双向联想记忆

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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