New and improved results for recurrent neural networks with interval time-varying delay

In this paper, the problem of stability analysis for a class of static recurrent neural networks with interval time-varying delay is considered. By constructing a newly augmented Lyapunov–Krasovskii functional containing triple integral terms and utilizing the inverses of first-order and squared reciprocally convex parameters techniques and zero equality, new and improved delay-dependent stability criteria are proposed to guarantee the asymptotic stability of the concerned networks with the framework of linear matrix inequalities (LMIs). Finally, some numerical examples are given to illustrate the effectiveness of the proposed methods.