Fixed time synchronization of delayed chaotic neural networks by using active adaptive control. (29th July 2021)

• Record Type: Journal Article
• Authors: Su, Haipeng; Luo, Runzi; Fu, Jiaojiao; Huang, Meichun
• Is Part Of: International journal of adaptive control and signal processing. Volume 35:Number 10(2021)
• Page Start: 2040
• Page End: 2055
• Publication Date: 2021-07-29
• Subjects: adaptive control -- chaotic neural system -- fixed time synchronization; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3307
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: This article aims to investigate the fixed time synchronization of a class of chaotic neural systems by way of adaptive control method. Using Lyapunov stability theory, a new fixed time stability theorem which plays an important role on the synchronization scheme is presented at first. Then, combining the fixed time stability theorem and adaptive control technique, an adaptive control scheme has been developed to achieve the fixed time synchronization of chaotic neural systems. The proposed controllers assure the global convergence of the error dynamics in fixed‐time based on the Lyapunov stability theory. Furthermore, the proposed control strategy cannot only provide a fast convergence rate, but also afford a bounded convergence time which is unrelated to the initial values and easy to work out by using the simple time calculation formula. Finally, numerical simulations are presented by taking a typical two‐order chaotic neural system as an example to verify and demonstrate the effectiveness of the proposed scheme.