Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks

Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks

© 2020 Elsevier B.V. This paper analyzes the stability and passivity problems for a class of memristor-based fractional-order competitive neural networks (MBFOCNNs) by using Caputo's fractional derivation. Firstly, impulsive effects are taken well into account and effective analysis techniques...

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Main Authors: G. Rajchakit, P. Chanthorn, M. Niezabitowski, R. Raja, D. Baleanu, A. Pratap
Format: Journal
Published: 2020
Subjects:
Computer Science
Neuroscience
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090051072&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/70412
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-704122020-10-14T08:46:07Z Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks G. Rajchakit P. Chanthorn M. Niezabitowski R. Raja D. Baleanu A. Pratap Computer Science Neuroscience © 2020 Elsevier B.V. This paper analyzes the stability and passivity problems for a class of memristor-based fractional-order competitive neural networks (MBFOCNNs) by using Caputo's fractional derivation. Firstly, impulsive effects are taken well into account and effective analysis techniques are used to reflect the system's practically dynamic behavior. Secondly, by using the Lyapunov technique, some sufficient conditions are obtained by linear matrix inequalities (LMIs) to ensure the stability and passivity of the MBFOCNNs, which can be effectively solved by the LMI computational tool in MATLAB. Finally, two numerical models and their simulation results are given to illustrate the effectiveness of the proposed results. 2020-10-14T08:30:02Z 2020-10-14T08:30:02Z 2020-12-05 Journal 18728286 09252312 2-s2.0-85090051072 10.1016/j.neucom.2020.07.036 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090051072&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/70412
institution Chiang Mai University
building Chiang Mai University Library
continent Asia
country Thailand
Thailand
content_provider Chiang Mai University Library
collection CMU Intellectual Repository
topic Computer Science
Neuroscience
spellingShingle Computer Science
Neuroscience
G. Rajchakit
P. Chanthorn
M. Niezabitowski
R. Raja
D. Baleanu
A. Pratap
Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks
description © 2020 Elsevier B.V. This paper analyzes the stability and passivity problems for a class of memristor-based fractional-order competitive neural networks (MBFOCNNs) by using Caputo's fractional derivation. Firstly, impulsive effects are taken well into account and effective analysis techniques are used to reflect the system's practically dynamic behavior. Secondly, by using the Lyapunov technique, some sufficient conditions are obtained by linear matrix inequalities (LMIs) to ensure the stability and passivity of the MBFOCNNs, which can be effectively solved by the LMI computational tool in MATLAB. Finally, two numerical models and their simulation results are given to illustrate the effectiveness of the proposed results.
format Journal
author G. Rajchakit
P. Chanthorn
M. Niezabitowski
R. Raja
D. Baleanu
A. Pratap
author_facet G. Rajchakit
P. Chanthorn
M. Niezabitowski
R. Raja
D. Baleanu
A. Pratap
author_sort G. Rajchakit
title Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks
title_short Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks
title_full Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks
title_fullStr Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks
title_full_unstemmed Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks
title_sort impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks
publishDate 2020
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090051072&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/70412
_version_ 1681752897984921600

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