Asymptotic stability of static neural networks with interval time-varying delay based on LMI

Abstract In this paper, the asymptotic stability of static neural networks with interval time-varying delay is studied. An improved integral inequality based on orthogonal polynomials is proposed, in which three free vectors can be selected independently. A novel Lyapunov-Krasovskii functional containing more delayed state information, instant state information and integrated state information is constructed. Based on the improved non-convex technique, two less conservative delay-dependent stability criteria in terms of linear matrix inequalities (LMIs) are derived. The validity and superiority of the theoretical results derived in this paper are verified by numerical simulation.