Back to Search
Start Over
Global exponential convergence of delayed inertial Cohen-Grossberg neural networks.
- Source :
- Nonlinear Analysis: Modeling & Control; 2023, Vol. 28 Issue 6, p1062-1076, 15p
- Publication Year :
- 2023
-
Abstract
- In this paper, the exponential convergence of delayed inertial Cohen-Grossberg neural networks (CGNNs) is studied. Two methods are adopted to discuss the inertial CGNNs, one is expressed as two first-order differential equations by selecting a variable substitution, and the other does not change the order of the system based on the nonreduced-order method. By establishing appropriate Lyapunov function and using inequality techniques, sufficient conditions are obtained to ensure that the discussed model converges exponentially to a ball with the prespecified convergence rate. Finally, two simulation examples are proposed to illustrate the validity of the theorem results. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13925113
- Volume :
- 28
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Nonlinear Analysis: Modeling & Control
- Publication Type :
- Academic Journal
- Accession number :
- 174570867
- Full Text :
- https://doi.org/10.15388/namc.2023.28.33431