title: Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.
abstract: Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.
authors.affiliation: School of Automation, China University of Geosciences, Wuhan, China.
authors.forename: Leimin
authors.initials: L
authors.lastname: Wang
authors.affiliation: School of Automation, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Shen
authors.affiliation: College of Mathematics and Statistics, South-Central University for Nationalities, Wuhan, China.
authors.forename: Guodong
authors.initials: G
authors.lastname: Zhang
keywords: Adaptive control; Delays; Neural networks; Stability criteria; State feedback