Global Asymptotic Stability and Robust Stability of a Class of Cohen–Grossberg Neural Networks With Mixed Delays

This paper is concerned with the global asymptotic stability of a class of Cohen-Grossberg neural networks with both multiple time-varying delays and continuously distributed delays. Two classes of amplification functions are considered, and some sufficient stability criteria are established to ensure the global asymptotic stability of the concerned neural networks, which can be expressed in the form of linear matrix inequality and are easy to check. Furthermore, some sufficient conditions guaranteeing the global robust stability are also established in the case of parameter uncertainties.