New results on finite-/fixed-time synchronization of delayed memristive neural networks with diffusion effects

In this paper, we further investigate the finite-/fixed-time synchronization (FFTS) problem for a class of delayed memristive reaction-diffusion neural networks (MRDNNs). By utilizing the state-feedback control techniques, and constructing a general Lyapunov functional, with the help of inequality t...

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Main Authors: Yinjie Qian, Lian Duan, Hui Wei
Format: Article
Language:English
Published: AIMS Press 2022-07-01
Series:AIMS Mathematics
Subjects:
Online Access:https://www.aimspress.com/article/doi/10.3934/math.2022931?viewType=HTML
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author Yinjie Qian
Lian Duan
Hui Wei
author_facet Yinjie Qian
Lian Duan
Hui Wei
author_sort Yinjie Qian
collection DOAJ
description In this paper, we further investigate the finite-/fixed-time synchronization (FFTS) problem for a class of delayed memristive reaction-diffusion neural networks (MRDNNs). By utilizing the state-feedback control techniques, and constructing a general Lyapunov functional, with the help of inequality techniques and the finite-time stability theory, novel criteria are established to realize the FFTS of the considered delayed MRDNNs, which generalize and complement previously known results. Finally, a numerical example is provided to support the obtained theoretical results.
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spelling doaj.art-5ab2e423130e4f5ea0a3ab2235f584852022-12-22T03:41:08ZengAIMS PressAIMS Mathematics2473-69882022-07-0179169621697410.3934/math.2022931New results on finite-/fixed-time synchronization of delayed memristive neural networks with diffusion effectsYinjie Qian0Lian Duan1Hui Wei2School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232001, ChinaIn this paper, we further investigate the finite-/fixed-time synchronization (FFTS) problem for a class of delayed memristive reaction-diffusion neural networks (MRDNNs). By utilizing the state-feedback control techniques, and constructing a general Lyapunov functional, with the help of inequality techniques and the finite-time stability theory, novel criteria are established to realize the FFTS of the considered delayed MRDNNs, which generalize and complement previously known results. Finally, a numerical example is provided to support the obtained theoretical results.https://www.aimspress.com/article/doi/10.3934/math.2022931?viewType=HTMLmemristive neural networkfinite-/fixed-time synchronizationdiffusion effect
spellingShingle Yinjie Qian
Lian Duan
Hui Wei
New results on finite-/fixed-time synchronization of delayed memristive neural networks with diffusion effects
AIMS Mathematics
memristive neural network
finite-/fixed-time synchronization
diffusion effect
title New results on finite-/fixed-time synchronization of delayed memristive neural networks with diffusion effects
title_full New results on finite-/fixed-time synchronization of delayed memristive neural networks with diffusion effects
title_fullStr New results on finite-/fixed-time synchronization of delayed memristive neural networks with diffusion effects
title_full_unstemmed New results on finite-/fixed-time synchronization of delayed memristive neural networks with diffusion effects
title_short New results on finite-/fixed-time synchronization of delayed memristive neural networks with diffusion effects
title_sort new results on finite fixed time synchronization of delayed memristive neural networks with diffusion effects
topic memristive neural network
finite-/fixed-time synchronization
diffusion effect
url https://www.aimspress.com/article/doi/10.3934/math.2022931?viewType=HTML
work_keys_str_mv AT yinjieqian newresultsonfinitefixedtimesynchronizationofdelayedmemristiveneuralnetworkswithdiffusioneffects
AT lianduan newresultsonfinitefixedtimesynchronizationofdelayedmemristiveneuralnetworkswithdiffusioneffects
AT huiwei newresultsonfinitefixedtimesynchronizationofdelayedmemristiveneuralnetworkswithdiffusioneffects