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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks
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Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks

机译：基于忆阻器的时滞神经网络的有限时间稳定与自适应控制

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摘要

Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.
机译：有限时间稳定性问题一直是控制和系统工程中的热门话题。本文通过两种控制方法处理基于忆阻器的延迟神经网络（MDNN）的有限时间稳定问题。首先，为了在有限时间内实现MDNN的稳定，提出了一种延迟状态反馈控制器。然后，将一种新颖的自适应策略应用于时滞控制器，并利用自适应控制律也可以实现MDNN的有限时间稳定。推导了一些易于验证的代数准则，以确保MDNN在有限时间内的稳定性，并给出了建立时间函数的估计。此外，给出了一些有限时间稳定性的结果，这是我们对于无延迟的基于忆阻器的神经网络（MNN）和神经网络的特殊情况。最后，提供了三个例子来说明理论结果。

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