A Novel Condition for Stability of Cohen-Grossberg Neural Networks of Neutral-Type with Discrete Delays

Abstract

In this study, global stability analysis is carried out for neutral-type Cohen-Grossberg neural systems with discrete time delays in state of neurons and discrete neutral delays in the time derivative of states of neurons. A proper Lyapunov functional is utilized to derive a novel sufficient condition for global asymptotic stability of this class of neural networks. This new stability criterion completely relies on the system elements of this neural network model, which is also independent of time and neutral delays. Therefore, the result derived in this work may be easily justified and verified as it establishes a simple algebraic relationship among the network components of the considered neural network.