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

被引：0
|
作者：
Tan G. [1 ]
Wang J. [1 ,2 ]
Wang Z. [1 ]
机构：
[1] College of Information Science and Engineering, Northeastern University, Shenyang
[2] School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou
来源：
Neurocomputing | 2021年 / 398卷
基金：
中国国家自然科学基金;
关键词：
L[!sub]2[!/sub]–L[!sub]∞[!/sub] performance; Neural networks; Second-order Bessel–Legendre inequality; State estimation; Time delay;
D O I：
10.1016/j.neucom.2020.02.059
中图分类号：
学科分类号：
摘要：
This paper is concerned with the L2–L∞ 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 L2–L∞ performance. As a result, the estimator gain matrix and the optimal L2–L∞ performance level are obtained. Simulation results are finally shown to illustrate the effectiveness of the proposed approach. © 2020 Elsevier B.V.
引用
收藏
页码：166 / 171
页数：5
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