Globally exponential stability of impulsive neural networks with given convergence rate

Authors:
Chengyan Liu;Xiaodi Li;Xilin Fu
Affiliations:
Department of Mathematics, Shandong Normal University, Ji'nan, China;Department of Mathematics, Shandong Normal University, Ji'nan, China;Department of Mathematics, Shandong Normal University, Ji'nan, China
Venue:
Advances in Artificial Neural Systems
Year:
2013

Citing 6
Cited 0

Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence

Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Global exponential stability and global attractivity of impulsive Hopfield neural networks with time delays

Journal of Computational and Applied Mathematics
On global exponential stability for impulsive cellular neural networks with time-varying delays

Computers & Mathematics with Applications
Exponential stability of impulsive discrete systems with time delay and applications in stochastic neural networks: A Razumikhin approach

Neurocomputing
Stability of impulsive control systems with time delay

Mathematical and Computer Modelling: An International Journal
Exponential synchronization of chaotic neural networks with mixed delays and impulsive effects via output coupling with delay feedback

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper deals with the stability problem for a class of impulsive neural networks. Some sufficient conditions which can guarantee the globally exponential stability of the addressed models with given convergence rate are derived by using Lyapunov function and impulsive analysis techniques. Finally, an example is given to show the effectiveness of the obtained results.