Improved Exponential Stability for Delayed Neural Networks With Large Delay Based on Relaxed Piecewise Lyapunov-Krasovskii Functional

Abstract

In this brief, the stability of neural networks with switching between small and large time delays is studied by developing an improved exponential stability criterion. Firstly, the delayed neural network (DNN) with small delay (SD) and large delay (LD) is modeled as a switched DNN. Then, based on an augmented piecewise Lyapunov-Krasovskii functional with LD-based terms considering relaxed switching constraints, and Wiritinger-based inequality, a stability criterion with less conservatism is developed. Finally, a numerical example is provided to demonstrate the superiority and effectiveness of the proposed method.