A graph-theoretic approach to exponential stability of stochastic BAM neural networks with time-varying delays

2015 ◽  
Vol 27 (7) ◽  
pp. 2055-2063 ◽  
Author(s):  
Huan Su ◽  
Yuwei Zhao ◽  
Wenxue Li ◽  
Xiaohua Ding
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Theoretic Approach ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Graph Theoretic ◽  
Graph Theoretic Approach

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

2020 ◽  
Vol 25 (5) ◽  
Author(s):  
Iswarya Manickam ◽  
Raja Ramachandran ◽  
Grienggrai Rajchakit ◽  
Jinde Cao ◽  
Chuangxia Huang
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Sufficient Conditions ◽  
Theoretic Approach ◽  
Markovian Jump ◽  
Markovian Jumping ◽  
Markovian Jump Parameters ◽  
Graph Theoretic ◽  
Mixed Time Delays ◽  
Graph Theoretic Approach

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.

Sign in / Sign up

Export Citation Format

Share Document