Global exponential stability of high-order Hopfield neural networks with state-dependent impulses

Zhilong He, Chuandong Li, Hongfei Li and Qiangqiang Zhang

Physica A: Statistical Mechanics and its Applications, 2020, vol. 542, issue C

Abstract: In this paper, we discuss the stability of high-order Hopfield neural networks with state-dependent impulses. Under some necessary assumptions, that every solution of the considered system intersects each impulsive surface exactly once is proved. Meanwhile, by using B-equivalence method, the considered system can be simplified to a system with fixed-time impulses. Moreover, some sufficient criteria are derived to ensure the stability between high-order Hopfield neural networks with state-dependent impulses and the corresponding system with fixed-time impulses. The main results show that the stability of high-order Hopfield neural networks with state-dependent impulses maintains no matter the stable continuous subsystems with unstabilizing impulses or the unstable continuous subsystems with stabilizing impulses. Finally, some numerical examples are given to illustrate the effectiveness of our results.

Keywords: elsarticle.cls; High-order Hopfield neural networks; State-dependent impulses; B-equivalence method; Exponential stability (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:542:y:2020:i:c:s037843711931917x

DOI: 10.1016/j.physa.2019.123434

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