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具有混合时变延迟的耦合中立型神经网络的全局固定时间同步

Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays.

作者信息

Zheng Mingwen, Li Lixiang, Peng Haipeng, Xiao Jinghua, Yang Yixian, Zhang Yanping, Zhao Hui

机构信息

School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.

School of Mathematics and Statistics, Shandong University of Technology, Zibo 255000, China.

出版信息

PLoS One. 2018 Jan 25;13(1):e0191473. doi: 10.1371/journal.pone.0191473. eCollection 2018.

Abstract

This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results.

摘要

本文主要研究一类具有混合时变延迟的耦合中立型神经网络通过不连续反馈控制器实现全局固定时间同步。与传统中立型神经网络模型相比,本文中的模型更具一般性。设计了一类通用的不连续反馈控制器。借助固定时间同步的定义、右上导数和定义的一个简单李雅普诺夫函数,推导了一些易于验证且可扩展的同步准则,以保证驱动系统和响应系统之间的固定时间同步。最后,给出了两个数值模拟来验证结果的正确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64bc/5784957/51d0548f0171/pone.0191473.g001.jpg

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