Graph-theoretic approach to exponential synchronization of stochastic reaction–diffusion Cohen–Grossberg neural networks with time-varying delays

2016 ◽  
Vol 177 ◽  
pp. 179-187 ◽  
Author(s):  
Huihui Song ◽  
Dongdong Chen ◽  
Wenxue Li ◽  
Yanbin Qu
Keyword(s):  
Neural Networks ◽  
Reaction Diffusion ◽  
Exponential Synchronization ◽  
Theoretic Approach ◽  
Time Varying ◽  
Graph Theoretic ◽  
Graph Theoretic Approach ◽  
Stochastic Reaction

Exponential Synchronization of Stochastic Reaction-Diffusion Fuzzy Cohen-Grossberg Neural Networks With Time-Varying Delays Via Periodically Intermittent Control

2013 ◽  
Vol 135 (6) ◽  
Author(s):  
Qintao Gan ◽  
Yang Li
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Reaction Diffusion ◽  
Exponential Synchronization ◽  
Intermittent Control ◽  
Time Varying ◽  
Synchronization Problem ◽  
Diffusion Effects ◽  
Stochastic Reaction ◽  
Delay Partitioning

In this paper, the exponential synchronization problem for fuzzy Cohen-Grossberg neural networks with time-varying delays, stochastic noise disturbance, and reaction-diffusion effects are investigated. By introducing a novel Lyapunov-Krasovskii functional with the idea of delay partitioning, a periodically intermittent controller is developed to derive sufficient conditions ensuring the addressed neural networks to be exponentially synchronized in terms of p-norm. The results extend and improve upon earlier work. A numerical example is provided to show the effectiveness of the proposed theories.

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