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时变时滞混沌神经网络的采样同步 被引量：1

Sampled-data synchronization for chaotic neural networks with time-varying delays
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摘要 研究了时变时滞混沌神经网络的采样同步问题。根据Lyapunov稳定性理论和输入延迟方法构造了新的Lyapunov泛函,得到了基于LMIs(线性矩阵不等式)形式且保守性更小的同步准则。通过MATLAB软件求解LMIs,得到了合理的采样控制器,使得该混沌神经网络在较大的采样间隔达到同步。数值仿真表明了该方法的优越性和有效性。 This paper investigated the problem of sampled-data synchronization for chaotic neural networks with time-varying delays. Based on the Lyapunov stability theory and the input delay method,it constructed a new Lyapunov functional. It exhibited the less conservative synchronization criterion in terms of LMIs（linear matrix inequalities）. Through utilizing the MATLAB software to solve LMIs,it can obtain the desired sampled-data controller and ensure the synchronization of the chaotic system under a bigger sampling interval. Besides,numerical simulations show the advantage and effectiveness of the proposed method.
作者 聂瑞兴 孙志毅 王健安 路瑶 张文煜
机构地区 太原科技大学电子信息工程学院
出处 《计算机应用研究》 CSCD 北大核心 2014年第7期2040-2043,2047,共5页 Application Research of Computers
关键词 神经网络 采样同步 LYAPUNOV泛函 线性矩阵不等式 neural networks sampled-data synchronization Lyapunov functional LMIs（linear matrix inequalities）
分类号 TP183 [自动化与计算机技术—控制理论与控制工程]
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参考文献17

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计算机应用研究
2014年 第7期

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