New stability criteria for discrete-time recurrent neural networks with time-varying delay

Abstract

Abstract This paper studies the problem of stability analysis for discrete-time recurrent neural networks (DRNNs) with time-varying delays. By defining a more general type of Lyapunov functionals and combining the discrete Jensen inequality, a new less conservative delay-dependent stability criterion is established in terms of linear matrix inequalities (LMIs) under a weak assumption on the activation functions. By using a delay decomposition method, a further improved stability criterion is also derived. It is shown that the newly obtained results are less conservative than the existing ones. Meanwhile, the computational complexity of the newly obtained stability conditions is reduced since less variables are involved. A numerical example is given to illustrate the effectiveness and the benefits of the proposed method.