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基于随机采样数据控制器的控制分组丢失和时变时滞神经网络同步。

Synchronization of Neural Networks With Control Packet Loss and Time-Varying Delay via Stochastic Sampled-Data Controller.

出版信息

IEEE Trans Neural Netw Learn Syst. 2015 Dec;26(12):3215-26. doi: 10.1109/TNNLS.2015.2425881. Epub 2015 May 8.

Abstract

This paper addresses the problem of exponential synchronization of neural networks with time-varying delays. A sampled-data controller with stochastically varying sampling intervals is considered. The novelty of this paper lies in the fact that the control packet loss from the controller to the actuator is considered, which may occur in many real-world situations. Sufficient conditions for the exponential synchronization in the mean square sense are derived in terms of linear matrix inequalities (LMIs) by constructing a proper Lyapunov-Krasovskii functional that involves more information about the delay bounds and by employing some inequality techniques. Moreover, the obtained LMIs can be easily checked for their feasibility through any of the available MATLAB tool boxes. Numerical examples are provided to validate the theoretical results.

摘要

本文针对时变时滞神经网络的指数同步问题进行了研究。考虑了具有随机时变采样间隔的采样数据控制器。本文的新颖之处在于考虑了从控制器到执行器的控制数据包丢失,这种情况在许多实际情况下可能会发生。通过构造一个适当的包含更多关于延迟边界信息的李雅普诺夫-克拉索夫斯基泛函,并运用一些不等式技术,本文以均方指数同步的充分条件的形式给出了线性矩阵不等式(LMI)。此外,通过任何可用的 MATLAB 工具箱都可以很容易地检查获得的 LMI 是否可行。数值示例验证了理论结果。

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