Globally Exponential Stability of Uncertain Memristor-based Recurrent Neural Networks with Unbounded Time-varying Delays

This paper studies the globally exponential stability of the equilibrium point for uncertain memristor-based recurrent neural networks (MRNN) with unbounded time-varying delay. The MRNN in this paper is the extension of classical MRNN since the uncertain factors and unbounded time-varying delay are considered. Under some assumptions for the MRNN, the equilibrium point of MRNN is proved to be globally exponentially stable by the Lyapunov method. A numerical experiment is performed to show the proposed result.