State Estimation for Static Neural Networks with Time-Varying Delay via Quadratic Function Negative-Determination Lemma

被引：0
|
作者：
Lao, Yong-He [1 ,2 ,3 ]
Xiong, Du [1 ,2 ,3 ]
Jin, Li [1 ,2 ,3 ]
Zhang, Chuan-Ke [1 ,2 ,3 ]
机构：
[1] China Univ Geosci, Sch Automat, Wuhan 430074, Peoples R China
[2] Hubei Key Lab Adv Control & Intelligent Automat C, Wuhan 430074, Peoples R China
[3] Minist Educ, Engn Res Ctr Intelligent Technol Geoexplorat, Wuhan 430074, Peoples R China
来源：
2022 41ST CHINESE CONTROL CONFERENCE (CCC) | 2022年
基金：
中国国家自然科学基金;
关键词：
Static Neural Networks; State Estimation; Time-Varying Delay; Reciprocally Convex Matrix Inequality; Quadratic Function Negative-Determination Lemma; STABILITY ANALYSIS; INEQUALITY; DESIGN;
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：
The H-infinity performance state estimation for static neural networks with time-varying delays is studied in this paper. Firstly, an augmented Lyapunov-Krasovskii functional (LKF) with the triple integral term and the delay-product-type term is constructed. Secondly, a generalized reciprocally convex matrix inequality is employed to deal with the derivative of the LKF. After that, by utilizing the relaxed quadratic function negative-definiteness determination method to dispose of the time derivative of the delay-product-type term, a less conservative state estimation criterion is obtained. Finally, the effectiveness of the proposed method is shown through a numerical example.
引用
收藏
页码：127 / 132
页数：6
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