Stability Analysis of Discrete-Time Delayed Neural Networks via A Cubic Function Negative-Determination Lemma

This paper is concerned with the stability analysis of discrete-time neural networks with a time-varying delay. Firstly, an improved augmented Lyapunov-Krasovskii functional (LKF) is established by adding single/double-summation terms. Then, by utilizing summation inequalities, the forward difference of the constructed functional is estimated as a cubic function with respect to the delay. By applying the cubic function negative-determination lemma, a less conservative stability criterion is obtained, whose advantages are demonstrated via a numerical example.