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基于混合控制的复值忆阻神经网络的有限时间同步。

Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control.

出版信息

IEEE Trans Neural Netw Learn Syst. 2022 Aug;33(8):3938-3947. doi: 10.1109/TNNLS.2021.3054967. Epub 2022 Aug 3.

Abstract

The finite-time synchronization problem is investigated for the master-slave complex-valued memristive neural networks in this article. A novel Lyapunov-function based finite-time stability criterion with impulsive effects is proposed and utilized to design the decentralized finite-time synchronization controller. Not only the settling time but also the attractive domain with respect to the impulsive gain and average impulsive interval, as well as initial values is derived according to the sufficient synchronization condition. Two examples are outlined to illustrate the validity of our hybrid control strategy.

摘要

本文研究了主从复值忆阻神经网络的有限时间同步问题。提出了一种新的基于李雅普诺夫函数的具有脉冲效应的有限时间稳定性判据,并利用该判据设计了分散式有限时间同步控制器。根据充分同步条件,不仅推导出了关于脉冲增益和平均脉冲间隔以及初始值的 Settling Time,还推导出了吸引域。通过两个实例说明了我们的混合控制策略的有效性。

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