Stabilization of Periodically Intermittent Discrete Noises and Application in Neural Networks

Author (Mao, IEEE Trans. Autom. Control 2016) opens up the new chapter of discrete stochastic stabilization. In addition, intermittent control can reduce the control cost effectively. Inspired by the thoughts of discrete stochastic stabilization and periodically intermittent control, based on discre...

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Bibliographic Details
Main Authors: Zhiyou Liu, Xinbin Li, Zhigang Lu, Lichao Feng, Xianhui Meng
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8779629/
Description
Summary:Author (Mao, IEEE Trans. Autom. Control 2016) opens up the new chapter of discrete stochastic stabilization. In addition, intermittent control can reduce the control cost effectively. Inspired by the thoughts of discrete stochastic stabilization and periodically intermittent control, based on discrete observations of systems states, we can design periodically intermittent discrete time noises to almost surely exponentially stabilize an unstable differential system with the global Lipschitz condition using the methods of comparison principle and stochastic analysis. Up to now, this brief is the first to investigate the cross effects of discrete stochastic noises and periodically intermittent control for unstable differential systems, which can fill up with the gap of these two fields. Moreover, this brief applies the stabilization assertions to recurrent neural networks.
ISSN:2169-3536