Global exponential stability in Lagrange sense for a class of neural networks
Qi Luo · Journal of Nanjing University of Information Science & Technology · 2009
Considering three types of bounded activation functions,and employing appropriate Lyapunoy functions method and inequality analyzing technique,this paper studies the global exponential stability in Lagrange sense for a class of neutral-type Cohen-Grossberg neural networks (NCGNN) with time-varying delays.Then several global exponential attractive sets in which all trajectories converge are obtained and the structural demonstrations of the system model are also presented.These results apply to the analysis of both monostable and multistable neural networks.Finally,some numerical examples as well as their simulation are given to verify our results.