Exponential stability of periodic solution for stochastic neural networks involving multiple time-varying delays-Reference-Cited by-同舟云学术

Exponential stability of periodic solution for stochastic neural networks involving multiple time-varying delays

Published:2024 Issue:6 Volume:9 Page:14932-14948
ISSN:2473-6988
Container-title:AIMS Mathematics
language:
Short-container-title:MATH

Author:

Zhou Zhigang¹,Wan Li¹,Zhang Qunjiao¹,Fu Hongbo¹,Li Huizhen¹,Zhou Qinghua²

Affiliation:

1. Research Center of Nonlinear Science, Research Center for Applied Mathematics and Interdisciplinary Sciences, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430073, China

2. School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China

Abstract

<abstract><p>This paper discusses the exponential stability of periodic solutions for stochastic neural networks with multiple time-varying delays. For these networks, sufficient conditions in the linear matrix inequality forms are rare in the literature. We constructed an appropriate Lyapunov-Krasovskii functional to eliminate the items with multiple delays and establish some sufficient conditions in linear matrix inequality forms, to ensure exponential stability of the periodic solutions. Several examples are provided to demonstrate that our results are effective and less conservative than previous ones.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

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