New delay-interval-dependent stability criteria for static neural networks with time-varying delays

Abstract

This paper introduces an effective approach to study the stability of static neural networks with interval time-varying delay using delay partitioning approach and tighter integral inequality lemma. By decomposing the delay interval into multiple equidistant subintervals and multiple nonuniform subintervals, some suitable Lyapunov–Krasovskii functionals are constructed on these intervals. A set of novel sufficient conditions are obtained to guarantee the stability analysis issue for the considered system. These conditions are expressed in the framework of linear matrix inequalities, which heavily depend on the lower and upper bounds of the time-varying delay. It is shown, by comparing with existing approaches, that the delay-partitioning approach can largely reduce the conservatism of the stability results. Finally, three examples are given to show the effectiveness of the theoretical results.