A delay partitioning approach to delay-dependent stability analysis for neutral type neural networks with discrete and distributed delays

This paper is concerned with the stability analysis of neutral type neural networks with discrete and distributed delays. Some improved delay-dependent stability results are established by using a delay partitioning approach for the networks. By employing a new type of Lyapunov-Krasovskii functionals, new delay-dependent stability criteria are derived. All the criteria are expressed in terms of linear matrix inequalities (LMIs), which can be solved efficiently by using standard convex optimization algorithms. Finally, numerical examples are given to illustrate the less conservatism of the proposed method.