Novel stability criteria for discrete‐time delayed neural networks via extended negative‐definiteness approaches of matrix‐valued quadratic function

This article investigates the stability analysis of discrete-time neural networks with time-varying delays by utilization quadratic delay information. First, three extended negative-definiteness lemmas for matrix-valued function different matrices injection are established. Second, a novel delay-product-type Lyapunov functional asymmetric summation is developed to relax positive-definiteness functional. Then, proposed negative definite approaches utilized in combination some typical inequalities realize construction linear matrix inequalities. Based on these improved technologies, two delay-dependent criteria less conservatism and fewer computational burdens derived. Finally, several numerical examples presented show validity superiority methods.