dc.contributor.author | Yao, Wei | |
dc.contributor.author | Wang, Chunhua | |
dc.contributor.author | Sun, Yichuang | |
dc.contributor.author | Zhou, Chao | |
dc.contributor.author | Lin, Hairong | |
dc.date.accessioned | 2020-07-28T00:06:32Z | |
dc.date.available | 2020-07-28T00:06:32Z | |
dc.date.issued | 2020-12-01 | |
dc.identifier.citation | Yao , W , Wang , C , Sun , Y , Zhou , C & Lin , H 2020 , ' Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations ' , Applied Mathematics and Computation , vol. 386 , 125483 . https://doi.org/10.1016/j.amc.2020.125483 | |
dc.identifier.issn | 0096-3003 | |
dc.identifier.uri | http://hdl.handle.net/2299/23009 | |
dc.description | © 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/. | |
dc.description.abstract | 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. | en |
dc.format.extent | 18 | |
dc.format.extent | 470407 | |
dc.language.iso | eng | |
dc.relation.ispartof | Applied Mathematics and Computation | |
dc.subject | Exponential multistability | |
dc.subject | Memristive Cohen-Grossberg neural network | |
dc.subject | Stable equilibrium point | |
dc.subject | Stochastic parameter perturbation | |
dc.subject | Computational Mathematics | |
dc.subject | Applied Mathematics | |
dc.title | Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations | en |
dc.contributor.institution | Centre for Engineering Research | |
dc.contributor.institution | Communications and Intelligent Systems | |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.contributor.institution | Department of Engineering and Technology | |
dc.description.status | Peer reviewed | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85086829566&partnerID=8YFLogxK | |
rioxxterms.versionofrecord | 10.1016/j.amc.2020.125483 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |