Improved delay-dependent stability criteria for neural networks with discrete and distributed time-varying delays using a delay-partitioning approach

Abstract

This paper is focused on the problem of delay-dependent stability criteria for neural networks (NNs) with discrete and distributed time-varying delays. Firstly, by constructing a newly augmented Lyapunov–Krasovskii functionals with multiple integral terms, less conservative stability criteria are formulated in terms of linear matrix inequalities. Secondly, some improved delay-dependent stability results are obtained by dividing the discrete and distributed delays into multiple nonuniformly subintervals and using a novel activation function condition. Besides, by employing the idea of second-order convex combination and the property of quadratic convex function which has been not used in the previous papers of NNs with mixed time-varying delays, further improved delay-dependent stability conditions are proposed. Finally, two numerical examples are given to verify the effectiveness and superiority of our proposed main results.