Exponential synchronization of stochastic perturbed chaotic delayed neural networks

Authors:
Yonghui Sun;Jinde Cao;Zidong Wang
Affiliations:
Department of Mathematics, Southeast University, Nanjing 210096, China;Department of Mathematics, Southeast University, Nanjing 210096, China and Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK;Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK
Venue:
Neurocomputing
Year:
2007

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Abstract

In this paper, we deal with the exponential synchronization problem for a class of stochastic perturbed chaotic delayed neural networks. Based on the Lyapunov stability theory, by virtue of stochastic analysis, Halanay inequality for stochastic differential equations, drive-response concept and time-delay feedback control techniques, several sufficient conditions are proposed to guarantee the exponential synchronization of two identical chaotic delayed neural networks with stochastic perturbation. These conditions, which are expressed in terms of linear matrix inequalities, rely on the connection matrix in the drive networks as well as the suitable designed feedback gains in the response networks. Finally, a numerical example with its simulations are provided to illustrate the effectiveness of the presented synchronization scheme.