Stability and Stabilization of Delayed Neural Networks with Hybrid Impulses

Author:

Zou Kefa1ORCID,Li Xuechen1ORCID,Wang Nan1ORCID,Lou Jungang2ORCID,Lu Jianquan34ORCID

Affiliation:

1. School of Science, Xuchang University, Xuchang 461000, China

2. Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, School of Information Engineering, Huzhou University, Huzhou 313000, China

3. School of Mathematics, Southeast University, Nanjing 210096, China

4. Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong, School of Automation and Electrical Engineering, Linyi University, Linyi 276005, China

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.

Funder

Natural Science Foundation of Zhejiang Province

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

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