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Chin. Phys. B, 2008, Vol. 17(11): 4080-4090    DOI: 10.1088/1674-1056/17/11/022
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Synchronization of stochastically hybrid coupled neural networks with coupling discrete and distributed time-varying delays

Tang Yang (唐漾)a, Zhong Hui-Huang(钟恢凰)bFang Jian-An(方建安)a
a Department of Automation, Donghua University, Shanghai 201620, China; b Department of Communication, Donghua University, Shanghai 201620, China
Abstract  A general model of linearly stochastically coupled identical connected neural networks with hybrid coupling is proposed, which is composed of constant coupling, coupling discrete time-varying delay and coupling distributed time-varying delay. All the coupling terms are subjected to stochastic disturbances described in terms of Brownian motion, which reflects a more realistic dynamical behaviour of coupled systems in practice. Based on a simple adaptive feedback controller and stochastic stability theory, several sufficient criteria are presented to ensure the synchronization of linearly stochastically coupled complex networks with coupling mixed time-varying delays. Finally, numerical simulations illustrated by scale-free complex networks verify the effectiveness of the proposed controllers.
Keywords:  stochastically hybrid coupling      discrete and distributed time-varying delays      complex dynamical networks      chaotic neural networks  
Received:  29 April 2008      Revised:  03 June 2008      Accepted manuscript online: 
PACS:  05.45.Xt (Synchronization; coupled oscillators)  
  05.40.Jc (Brownian motion)  
  05.45.Gg (Control of chaos, applications of chaos)  
  05.45.Pq (Numerical simulations of chaotic systems)  
  07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)  
Fund: Project supported by the National Natural Science Foundation of China (Grant No 60874113).

Cite this article: 

Tang Yang (唐漾), Zhong Hui-Huang(钟恢凰), Fang Jian-An(方建安) Synchronization of stochastically hybrid coupled neural networks with coupling discrete and distributed time-varying delays 2008 Chin. Phys. B 17 4080

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