事件触发下混合时滞神经网络的状态估计
Year: 2020
Type: article
Abstract: The event-based state estimation problem was investigated for a class of neural networks with mixed delays. A novel event-triggering scheme depending on both the output and exponential decay function was designed to reduce the frequency of updating. In view of both the mixed delays and the event-triggering properties, a new state estimation error system was built. The exponential stability of the error system was derived with the Lyapunov function and the inequality technique. The Zeno phenomenon was analyzed and excluded. Finally, a numerical example and its simulations were presented to illustrate the effectiveness of the proposed approach.
Source: 应用数学和力学
Authors: 杜雨薇，李兵，宋乾坤, DU Yuwei, LI Bing, SONG Qiankun
Language: Chinese
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4232656938