Less conservative stability criteria for general neural networks through novel delay-dependent functional

Abstract

This work investigates the improved stability conditions for neural networks with time-varying delay. By the construction of newly augmented Lyapunov–Krasovskii functionals including the integral inequality and the use of the augmented zero equality approach, three improved results are proposed in the form of linear matrix inequalities. Two delay-dependent Lyapunov–Krasovskii functionals based on the integral inequality are proposed for the first time. Also, by utilizing the augmented zero equality approach, a less conservative result is obtained while reducing computation complexity. Through some numerical examples, the effectiveness and superiority of the proposed results are confirmed by comparing the existing works.