Improved delay-dependent robust passivity criteria for uncertain neural networks with discrete and distributed delays

This paper studies the problem of delay-dependent passivity for uncertain neural networks (UNNs) with discrete and distributed delays. Without considering free weighting matrices and multiple integral terms, which may cause more numbers of linear matrix inequalities (LMIs) and scalar decision variables. By constructing a suitable Lyapunov-Krasovskii functional (LKF) and combining with the reciprocally convex approach, some sufficient conditions are established in terms of LMIs. Compared with existing results, the derived criteria are more effective due to the application of delay partitioning approach which takes a full consideration of all available information in various delay intervals. Two simulation examples are given to illustrate the effectiveness of the proposed method.