The state estimation for discrete-time delayed standard neural networks model

被引：0
|
作者：
Zhu, Jin [1 ]
Li, Taifang [2 ]
Leng, Qiangkui [3 ]
机构：
[1] Bohai Univ, Coll Math & Sci, Jinzhou 121000, Peoples R China
[2] Bohai Univ, Coll Technol, Jinzhou 121000, Peoples R China
[3] Bohai Univ, Sch Informat Sci & Technol, Jinzhou 121000, Peoples R China
来源：
PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020) | 2020年
关键词：
Standard Neural Networks; Estimation; Asymptotically Stable; DEPENDENT STABILITY-CRITERIA; EXPONENTIAL STABILITY; SYNCHRONIZATION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, the problem of state estimation for discrete-time delayed standard neural networks model (DDSNNM) is investigated. By constructing suitable Lyapunov-Krasovskii functional, the state-feedback controller is designed such that the close-loop error system is asymptotically stable. The existence condition of the state estimator was proposed in the form of linear matrix inequality, so the estimator gain matrix can be obtained easily by solving the LMI. The simulation is given to illustrate the effectiveness of our method.
引用
收藏
页码：2348 / 2353
页数：6
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