Impulsive Fractional Cohen-Grossberg Neural Networks: Almost Periodicity Analysis

dc.contributor.authorStamova, Ivanka
dc.contributor.authorSotirov, Sotir
dc.contributor.authorSotirova, Evdokia
dc.contributor.authorStamov, Gani
dc.date.accessioned2021-09-25T23:33:27Z
dc.date.available2021-09-25T23:33:27Z
dc.date.issued2021-07-27
dc.date.updated2021-09-25T23:33:29Z
dc.description.abstractIn this paper, a fractional-order Cohen–Grossberg-type neural network with Caputo fractional derivatives is investigated. The notion of almost periodicity is adapted to the impulsive generalization of the model. General types of impulsive perturbations not necessarily at fixed moments are considered. Criteria for the existence and uniqueness of almost periodic waves are proposed. Furthermore, the global perfect Mittag–Leffler stability notion for the almost periodic solution is defined and studied. In addition, a robust global perfect Mittag–Leffler stability analysis is proposed. Lyapunov-type functions and fractional inequalities are applied in the proof. Since the type of Cohen–Grossberg neural networks generalizes several basic neural network models, this research contributes to the development of the investigations on numerous fractional neural network models.
dc.description.departmentMathematics
dc.identifierdoi: 10.3390/fractalfract5030078
dc.identifier.citationFractal and Fractional 5 (3): 78 (2021)
dc.identifier.urihttps://hdl.handle.net/20.500.12588/683
dc.rightsAttribution 4.0 United States
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectfractional-order derivatives
dc.subjectimpulses
dc.subjectCohen–Grossberg neural networks
dc.subjectalmost periodicity
dc.subjectperfect Mittag–Leffler stability
dc.subjectrobustness
dc.titleImpulsive Fractional Cohen-Grossberg Neural Networks: Almost Periodicity Analysis
dc.typeArticle

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