Stability and Stabilization of Delayed Neural Networks with Hybrid Impulses. (21st November 2020)

• Record Type: Journal Article
• Authors: Zou, Kefa; Li, Xuechen; Wang, Nan; Lou, Jungang; Lu, Jianquan
• Other Names: Innocenti Giacomo Academic Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-11-21
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/8712027
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : In this paper, the stability and stabilization issues for a class of delayed neural networks with time-varying hybrid impulses are investigated. The hybrid effect of two types of impulses including both stabilizing and destabilizing impulses is considered simultaneously in the analysis of systems. To characterize the occurrence features of impulses, the concepts of average impulse interval and average impulse strength are employed. Based on the analysis of stability, a pinning impulsive controller which can ensure the global exponential stability of the studied neural networks is designed by pinning a small fraction of neurons. Finally, two numerical examples are given to illustrate the effectiveness of the proposed control schemes for delayed neural networks with hybrid impulses.