Exponential synchronization of stochastic perturbed chaotic delayed neural networks
Exponential synchronization of stochastic perturbed chaotic delayed neural networks
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DOI：
10.1016/j.neucom.2006.09.006
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发表时间：
2007-08
期刊：
影响因子：
6
通讯作者：
Yonghui Sun;Jinde Cao;Zidong Wang
中科院分区：
文献类型：
--
作者：
Yonghui Sun;Jinde Cao;Zidong Wang
研究了一类随机扰动混沌时滞神经网络的指数同步问题。基于李雅普诺夫稳定性理论，借助随机分析、随机微分方程的Halanay不等式、驱动-响应概念和时滞反馈控制技术，给出了两个具有随机扰动的混沌时滞神经网络指数同步的充分条件.用线性矩阵不等式表示的这些条件依赖于驱动网络中的连接矩阵以及响应网络中适当设计的反馈增益。最后，数值例子和仿真结果说明了所提出的同步方案的有效性。
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.