New global asymptotic stability criteria of Hopfield neural network with time-varying delays

Abstract

In this paper, the global stability of Hopfield neural networks with time-varying delays is investigated by utilizing Lyapunov functional method and the linear matrix inequality (LMI) technique. Distinct difference from other analytical approach lies in “linearization” of neural network model, by which the considered neural network model is transformed into a linear time-variant system. Then, a process, which is called parameterized first-order model transformation, is used to transform the linear process. Novel criteria for global asymptotic stability of the unique equilibrium point of Hopfield neural network with time-varying delays are obtained. the obtained results are less conservative and restrictive than those established in the earlier references. Numerical examples are given to show the effectiveness of our proposed method.