Novel criteria for robust stability of Cohen-Grossberg neural networks with multiple time delays

<p style='text-indent:20px;'>This research paper deals with the investigation of global robust stability results for Cohen-Grossberg neural networks involving multiple constant time delays. The activation functions in this network model are supposed to be set non-decreasing slope-bounded nonlinear and uncertainties parameters considered have bounded upper norms. By employing a proper positive definite Lyapunov-type functional using homeomorphism mapping theory, we propose some novel sets conditions that assure both existence, uniqueness asymptotic equilibrium points Cohen-Grossberg-type derived robustly stable mainly rely on examining relationships imposed valued interconnection matrices delayed network. These can certainly verified by various simple useful properties real interval matrices. Some comparisons made address key advantages these criteria over previously reported corresponding results. An instructive example is also examined observe novelty proposed criteria.</p>