A new result on L2-L∝ performance state estimation of neural networks with time-varying delay

被引：0
|
作者：
Tan, Guoqiang [1 ]
Wang, Jidong [1 ,2 ]
Wang, Zhanshan [1 ]
机构：
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China
[2] North China Univ Water Resources & Elect Power, Sch Elect Engn, Zhengzhou 450045, Peoples R China
来源：
NEUROCOMPUTING | 2020年 / 398卷
基金：
中国国家自然科学基金;
关键词：
L-2-L-infinity performance; State estimation; Time delay; Neural networks; Second-order Bessel-Legendre inequality; STABILITY ANALYSIS; LINEAR-SYSTEMS; INEQUALITY; DESIGN;
D O I：
10.1016/j.neucom.2020.02.059
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper is concerned with the L-2-L-infinity performance state estimation problem of delayed neural networks. Firstly, the second-order Bessel-Legendre inequality based on reciprocally convex approach is proposed. Secondly, based on the improved integral inequality, a new delay-dependent condition is derived, which ensures the asymptotic stability of estimation error system with L-2-L-infinity performance. As a result, the estimator gain matrix and the optimal L-2-L-infinity performance level are obtained. Simulation results are finally shown to illustrate the effectiveness of the proposed approach. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页码：166 / 171
页数：6
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