Sampled‐data synchronisation for memristive neural networks with multiple time‐varying delays via extended convex combination method-Reference-Cited by-同舟云学术

Sampled‐data synchronisation for memristive neural networks with multiple time‐varying delays via extended convex combination method

Published:2018-03 Issue:7 Volume:12 Page:922-932
ISSN:1751-8652
Container-title:IET Control Theory & Applications
language:en
Short-container-title:IET Control Theory & Applications

Author:

Zhang Ruimei¹,Zeng Deqiang²³,Zhong Shouming¹,Yu Yongbin⁴,Cheng Jun⁵

Affiliation:

1. School of Mathematics Sciences, University of Electronic Science and Technology of ChinaChengduSichuan611731People's Republic of China

2. Data Recovery Key Laboratory of Sichuan Province, College of Mathematics and Information Science, Neijiang Normal UniversityNeijiangSichuan641100People's Republic of China

3. Numerical Simulation Key Laboratory of Sichuan Province, College of Mathematics and Information Science, Neijiang Normal UniversityNeijiangSichuan641100People's Republic of China

4. School of Information and Software Engineering, University of Electronic Science and Technology of ChinaChengduSichuan610054People's Republic of China

5. School of Science, Hubei University for NationalitiesEnshiHubei445000People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Control and Optimization,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cta.2017.1172

Reference47 articles.

1. Memristor-The missing circuit element

2. The missing memristor found

3. Event‐triggered sampling control for stability and stabilization of memristive neural networks with communication delays;Zhang R.;Appl. Math. Comput.,2017

4. Exponential stabilisation of stochastic memristive neural networks under intermittent adaptive control

5. Synchronization of delayed memristive neural networks: robust analysis approach;Ho D.W.C.;IEEE Trans. Cybern.,2016

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