估计员
计算机科学
控制理论(社会学)
人工神经网络
厄米矩阵
国家(计算机科学)
基质(化学分析)
集合(抽象数据类型)
数学优化
数学
算法
人工智能
控制(管理)
纯数学
材料科学
复合材料
程序设计语言
统计
作者
Bing Li,Feiyang Liu,Qiankun Song,Dongpei Zhang,Huanhuan Qiu
标识
DOI:10.1016/j.neucom.2022.11.079
摘要
In this paper, the problem of state estimation is investigated for a class of discrete-time complex-valued neural networks (CVNNs) with both leakage delay and discrete time-varying delays. The signal transmission from output sensors to state estimator is implemented via a shared wireless network with limited communication resources. For the aim of reducing the consumption of limited communication resources, the transmission strategy based on dynamic event-triggering is introduced to determine when the updating of the output measurement should be carried out. By taking use of some properties of Hermitian matrix and constructing an appropriate Lyapunov–Krasovskii functional, a sufficient criterion is derived for ensuring the asymptotical stability of the estimation error system without separating the CVNN to its real-part system and imagination one is derived, which is quite different from those approach used in exiting literature. The gain matrix for estimator is designed by resorting to a set of feasible solutions of linear matrix inequalities (LMIs) with complex-valued variables. A numerical example and its simulation results are given to illustrate the validity of the theoretical result.
科研通智能强力驱动
Strongly Powered by AbleSci AI