Improved Stability Criteria for Discrete-Time Delayed Neural Networks via Novel Lyapunov–Krasovskii Functionals

This article investigates the stability problem for discrete-time neural networks with a time-varying delay by focusing on developing new Lyapunov–Krasovskii (L–K) functionals. A novel L–K functional is deliberately tailored from two aspects: 1) the quadratic term and 2) the single-summation term. When the variation of the discrete-time delay is further considered, the constant matrix involved in the quadratic term is extended to be a delay-dependent one. All these innovations make a contribution to a quadratic function with respect to the delay from the forward differences of L–K functionals. Consequently, tractable stability criteria are derived that are shown to be more relaxed than existing results via numerical examples.