Delay-dependent passivity criterion for discrete-time delayed standard neural network model

In this paper, the problem of robust passivity for discrete-time delayed standard neural network model (DDSNNM) with time-varying delays and norm-bounded parameters uncertainties is investigated. The model is the interconnection of a linear dynamic system and a bounded static delayed nonlinear operator. The DDSNNM is applied to analyze the passivity of discrete-time recurrent neural networks and synthesize the state-feedback passive controller for discrete-time nonlinear system modeled by the neural networks. By constructing suitable Lyapunov–Krasovskii functional, the delay-dependent passivity criterion for discrete-time delayed standard neural network model is obtained in terms of linear matrix inequality. Numerical examples are given to illustrate the effectiveness of the proposed methods.