Novel stability criteria for uncertain delayed Cohen–Grossberg neural networks using discretized Lyapunov functional

This paper deals with the stability analysis of delayed uncertain Cohen–Grossberg neural networks (CGNN). The proposed methodology consists in obtaining new robust stability criteria formulated as linear matrix inequalities (LMIs) via the Lyapunov–Krasovskii theory. Particularly one stability criterion is derived from the selection of a parameter-dependent Lyapunov–Krasovskii functional, which allied with the Gu’s discretization technique and a simple strategy that decouples the system matrices from the functional matrices, assures a less conservative stability condition. Two computer simulations are presented to support the improved theoretical results.