Global exponential stability of discrete-time CohenGrossberg neural networks ?
Discrete-time versions of the continuous-time CohenGrossberg neural networks (CGNNs) are formulated and studied in this paper. Several sufficient conditions are obtained to ensure the global exponential stability of the discrete-time systems of CGNNs with and without delays based on Lyapunov methods. The obtained results have not assume the symmetry of the connection matrix, and monotonicity and the differentiability of the activation functions.
global exponential stability discrete-time cohengrossberg neural networks lyapunov functions