Globally Exponential Stability of Impulsive Neural Networks with Given Convergence Rate
Chengyan Liu, Xiaodi Li, Xilin Fu · Advances in Artificial Neural Systems · 2013
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.