Exponential Stability of Hopfield Neural Network Model with Non-Instantaneous Impulsive Effects-Reference-Cited by-同舟云学术

Exponential Stability of Hopfield Neural Network Model with Non-Instantaneous Impulsive Effects

Published:2023-01-22 Issue:2 Volume:12 Page:115
ISSN:2075-1680
Container-title:Axioms
language:en
Short-container-title:Axioms

Author:

Ma Rui¹^ORCID,Fečkan Michal²³^ORCID,Wang Jinrong¹^ORCID

Affiliation:

1. Department of Mathematics, Guizhou University, Guiyang 550025, China

2. Department of Mathematical Analysis and Numerical Mathematics, Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Mlynská Dolina, 842 48 Bratislava, Slovakia

3. Mathematical Institute, Slovak Academy of Sciences, Štefánikova 49, 814 73 Bratislava, Slovakia

Abstract

We introduce a non-instantaneous impulsive Hopfield neural network model in this paper. Firstly, we prove the existence and uniqueness of an almost periodic solution of this model. Secondly, we prove that the solution of this model is exponentially stable. Finally, we give an example of this model.

Funder

Guizhou Data Driven Modeling Learning and Optimization Innovation Team

Slovak Research and Development Agency

Ministry of Education, Science, Research and Sport of the Slovak Republic

Publisher

MDPI AG

Subject

Geometry and Topology,Logic,Mathematical Physics,Algebra and Number Theory,Analysis

Link

https://www.mdpi.com/2075-1680/12/2/115/pdf

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