School of Automation, China University of Geosciences, Wuhan, China.
School of Automation, Huazhong University of Science and Technology, Wuhan, China.
IEEE Trans Neural Netw Learn Syst. 2017 Nov;28(11):2648-2659. doi: 10.1109/TNNLS.2016.2598598.
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.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.
有限时间稳定性问题一直是控制和系统工程领域的热门话题。本文通过两种控制方法研究基于忆阻器的时滞神经网络（MDNNs）的有限时间稳定性问题。首先，为了实现 MDNNs 的有限时间稳定，提出了一个时滞状态反馈控制器。然后，将一种新的自适应策略应用于时滞控制器，通过自适应控制律也可以实现 MDNNs 的有限时间稳定。推导出一些易于验证的代数判据来确保 MDNNs 在有限时间内的稳定性，并给出了镇定时间函数的估计。此外，还给出了作为特例的无延迟忆阻神经网络（MNNs）和神经网络的有限时间稳定性结果。最后，通过三个例子来说明理论结果。
