Novel Lagrange sense exponential stability criteria for time-delayed stochastic Cohen–Grossberg neural networks with Markovian jump parameters: A graph-theoretic approach

Nonlinear Analysis

卷:25

Iswarya Manickam¹  Raja Ramachandran¹  Chuangxia Huang²  Grienggrai Rajchakit³  Jinde Cao⁴ 

[1] Alagappa University;

[2] Changsha University of Science and Technology;

[3] Maejo University;

[4] Southeast University;

关键词: Cohen–Grossberg neural networks;    Lagrange stability;    graph theory;    discrete and distributed time delays;   

DOI  :  10.15388/namc.2020.25.16775

来源: DOAJ

【 摘 要 】

This paper concerns the issues of exponential stability in Lagrange sense for a class of stochastic Cohen–Grossberg neural networks (SCGNNs) with Markovian jump and mixed time delay effects. A systematic approach of constructing a global Lyapunov function for SCGNNs with mixed time delays and Markovian jumping is provided by applying the association of Lyapunov method and graph theory results. Moreover, by using some inequality techniques in Lyapunov-type and coefficient-type theorems we attain two kinds of sufficient conditions to ensure the global exponential stability (GES) through Lagrange sense for the addressed SCGNNs. Ultimately, some examples with numerical simulations are given to demonstrate the effectiveness of the acquired result.

【 授权许可】

Unknown