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Existence and global asymptotic stability of positive periodic solution of delayed Cohen-Grossberg neural networks
Version 2 2024-05-30, 08:50Version 2 2024-05-30, 08:50
Version 1 2014-12-17, 17:18Version 1 2014-12-17, 17:18
journal contribution
posted on 2024-05-30, 08:50 authored by LV Hien, TT Loan, BT Huyen Trang, Hieu TrinhHieu TrinhIn this paper, a class of periodic Cohen-Grossberg neural networks with discrete and distributed time-varying delays is considered. By an extension of the Lyapunov-Krasovskii functional method, a novel criterion for the existence and uniqueness and global asymptotic stability of positive periodic solution is derived in terms of M-matrix without any restriction on uniform positiveness of the amplification functions. Comparison and illustrative examples are given to illustrate the effectiveness of the obtained results. © 2014 Elsevier Inc. All rights reserved.
History
Journal
Applied Mathematics and ComputationVolume
240Pagination
200-212Location
Philadelphia, United StatesPublisher DOI
ISSN
0096-3003Language
engPublication classification
C Journal article, C1 Refereed article in a scholarly journalCopyright notice
2014, ElsevierPublisher
Elsevier Inc.Usage metrics
Categories
Keywords
Cohen-Grossberg neural networksM-matrixNon-autonomous systemsPeriodic solutionsTime-varying delaysScience & TechnologyPhysical SciencesMathematics, AppliedMathematicsDISTRIBUTED DELAYSEXPONENTIAL STABILITYDISCRETECRITERIADESIGNDYNAMICSLEAKAGE010203 Calculus of Variations, Systems Theory and Control Theory010204 Dynamical Systems in Applications090602 Control Systems, Robotics and Automation970109 Expanding Knowledge in EngineeringSchool of EngineeringDP130101532
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