Global stabilization of a class of fractional-order delayed bidirectional associative memory neural networks

Abstract

This paper focuses on the stabilization problem of a class of fractional-order bidirectional associative memory neural networks with time delays. Based on feedback control, a sufficient condition is derived to achieve the global stabilization of systems by using the fractional inequality, the Lyapunov stability theory, and the comparison principle. In particular, this kind of control scheme is proved to be robust in the presence of external disturbances when the feedback gains are sufficiently large. In addition, a condition is obtained to achieve the global quasi-stabilization of systems with some external disturbances, and the corresponding error bound is estimated. Finally, some numerical simulations are presented to verify the effectiveness of theoretical results.