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dc.contributor.authorYao, Wei
dc.contributor.authorWang, Chunhua
dc.contributor.authorSun, Yichuang
dc.contributor.authorZhou, Chao
dc.contributor.authorLin, Hairong
dc.date.accessioned2020-07-28T00:06:32Z
dc.date.available2020-07-28T00:06:32Z
dc.date.issued2020-12-01
dc.identifier.citationYao , 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.issn0096-3003
dc.identifier.otherPURE: 22040720
dc.identifier.otherPURE UUID: 4f8e89c5-5b11-43ce-9829-0f09ca6041e1
dc.identifier.otherScopus: 85086829566
dc.identifier.urihttp://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.abstractDue 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.extent18
dc.language.isoeng
dc.relation.ispartofApplied Mathematics and Computation
dc.subjectExponential multistability
dc.subjectMemristive Cohen-Grossberg neural network
dc.subjectStable equilibrium point
dc.subjectStochastic parameter perturbation
dc.subjectComputational Mathematics
dc.subjectApplied Mathematics
dc.titleExponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbationsen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85086829566&partnerID=8YFLogxK
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1016/j.amc.2020.125483
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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