This paper is concerned with the problem of stability analysis for a class of discrete-time
recurrent neural networks with time-varying delays. Under a weak assumption on the
activation functions and using a new Lyapunov functional, a delay-dependent condition
guaranteeing the global exponential stability of the concerned neural network is obtained in
terms of a linear matrix inequality. It is shown that this stability condition is less conservative
than some previous ones in the literature. When norm-bounded parameter uncertainties …

This brief is concerned with the analysis problem of global exponential stability in the mean
square sense for a class of linear discrete-time recurrent neural networks (DRNNs) with
stochastic delay. Different from the prior research works, the effects of both variation range
and probability distribution of the time delay are involved in the proposed method. First, a
modeling method is proposed by translating the probability distribution of the time delay into
parameter matrices of the transformed DRNN model, where the delay is characterized by a …