Global quasi-synchronization of complex-valued recurrent neural networks with time-varying delay and interaction terms

Abstract

In this article, the global quasi-synchronization of complex-valued recurrent neural networks (CVRNNs) with time-varying delays and interaction terms has been investigated. It is based on the standard Lyapunov stability theory and matrix measure method employed with the nonlinear Lipschitz activation functions. A sufficient condition for global quasi-synchronization of the complex-valued recurrent neural network model is shown in an effective way through a proper description of Lyapunov-stability technique. This article provides quite a new result for the CVRNNs having time-varying delays and interaction terms. Finally, a numerical example is considered to show the viability and unwavering quality of our theoretical results under several conditions.