Robust stability of uncertain stochastic complex-valued neural networks with additive time-varying delays

Abstract

In this paper, the robust stability problem for a class of uncertain stochastic complex-valued neural networks (USCVNNs) with additive time-varying delays (ATDs) is discussed. By constructing a suitable Lyapunov–Krasovskii functional (LKF), more time delay information is considered. By employing integral inequalities, some delay-dependent stability criteria are derived by converting USCVNNs into an equivalent real-valued uncertain stochastic neural networks. The obtained stability criterion is presented in the form of linear matrix inequalities (LMIs), which can be calculated through MATLAB LMI toolbox. Finally, the validity and feasibility of the proposed method are demonstrated by two numerical examples.