Global asymptotic synchronization of nonidentical fractional-order neural networks

Abstract

Abstract This paper investigates the global asymptotic synchronization problem of nonidentical fractional-order neural networks with Riemann–Liouville derivative. First, by utilizing the properties of fractional calculus and fractional Lyapunov direct method, some novel properties on the fractional calculus and the asymptotic stability theorem of reducing the conservatism for the non-autonomous fractional-order system with Riemann–Liouville derivative are proposed. Then, a new feedback controller is presented to guarantee the global asymptotic synchronization of nonidentical fractional-order neural networks. Via using the proposed the asymptotic stability theorem and matrix inequality techniques, sufficient conditions for global asymptotic synchronization of fractional-order neural networks are presented. Finally, numerical examples are used to demonstrate the effectiveness of the proposed synchronization control scheme for nonidentical fractional-order neural networks.