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Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations
Authors
Angeli
Aubin
+59 more
Cao
Chang
Chao Zhou
Chen
Chen
Cheng
Chunhua Wang
Cohen
Ding
Duan
Erichsen
Fang
Gopalsamy
Guo
Hairong Lin
Hopfield
Itoh
Kaslik
Li
Li
Li
Li
Lin
Lin
Liu
Liu
Liu
Lv
Melnyk
Nie
Nie
Nie
Qin
Tan
Wang
Wang
Wang
Wang
Wei Yao
Wu
Xu
Xu
Xu
Xu
Xu
Yang
Yang
Yang
Yang
Yang
Yao
Yichuang Sun
Yu
Zeng
Zeng
Zhao
Zhou
Zhou
Zhou
Publication date
1 December 2020
Publisher
'Elsevier BV'
Doi
Cite
Abstract
© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.Due to instability being induced easily by parameter disturbances of network systems, this paper investigates the multistability of memristive Cohen-Grossberg neural networks (MCGNNs) under stochastic parameter perturbations. It is demonstrated that stable equilibrium points of MCGNNs can be flexibly located in the odd-sequence or even-sequence regions. Some sufficient conditions are derived to ensure the exponential multistability of MCGNNs under parameter perturbations. It is found that there exist at least (w+2) l (or (w+1) l) exponentially stable equilibrium points in the odd-sequence (or the even-sequence) regions. In the paper, two numerical examples are given to verify the correctness and effectiveness of the obtained results.Peer reviewe
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Last time updated on 29/07/2020
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Last time updated on 05/09/2020