Skip to main content
Article
Almost Periodic Solutions of Cohen-Grossberg Neural Networks with Time-Varying Delay and Variable Impulsive Perturbations
Communications in Nonlinear Science and Numerical Simulation
  • Martin Bohner, Missouri University of Science and Technology
  • Gani Tr. Stamov
  • Ivanka M. Stamova
Abstract

In this paper, we consider the problem of existence of almost periodic solutions of impulsive Cohen-Grossberg neural networks with time-varying delays. The impulses are not at fixed moments, but are realized when the integral curves of solutions meet given hypersurfaces, i.e., the investigated model is with variable impulsive perturbations. Sufficient conditions for perfect stability of almost periodic solutions are derived. The main results are obtained by employing the Lyapunov-Razumikhin method and a comparison principle. In addition, the obtained results are extended to the uncertain case, and robust stability of almost periodic solutions is also investigated. An example is considered to demonstrate the effectiveness of our results.

Department(s)
Mathematics and Statistics
Keywords and Phrases
  • Almost periodic functions,
  • Cohen-Grossberg neural networks,
  • Lyapunov'Razumikhin method,
  • Perfect stability,
  • Uncertain terms,
  • Variable impulsive perturbations
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Elsevier B.V., All rights reserved.
Publication Date
1-1-2020
Publication Date
01 Jan 2020
Disciplines
Citation Information
Martin Bohner, Gani Tr. Stamov and Ivanka M. Stamova. "Almost Periodic Solutions of Cohen-Grossberg Neural Networks with Time-Varying Delay and Variable Impulsive Perturbations" Communications in Nonlinear Science and Numerical Simulation Vol. 80 (2020) ISSN: 1007-5704
Available at: http://works.bepress.com/martin-bohner/190/