H∞ performance state estimation of delayed static neural networks based on an improved proportional-integral estimator

被引：35

作者：

Tan, Guoqiang ^{[1
]}

Wang, Zhanshan ^{[1
]}

Li, Cong ^{[1
]}

机构：

[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2020年 / 370卷

基金：

中国国家自然科学基金;

关键词：

H-infinity performance; State estimation; Time-varying delay; Static neural networks; Proportional-integral estimator with exponential gain term; SYNCHRONIZATION; SYSTEMS; STABILITY; OBSERVER;

D O I：

10.1016/j.amc.2019.124908

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, an improved proportional-integral (PI) estimator is presented to analyze the problem of H-infinity performance state estimation of static neural networks with disturbance. An exponential gain term is added to the PI estimator, which leads to the convenience of analysis and design. In order to guarantee the H-infinity performance state estimation, a less conservative delay-dependent criterion is derived by using an improved reciprocally convex inequality. Finally, simulation results are given to verify the advantage of the presented approach. (C) 2019 Elsevier Inc. All rights reserved.

引用

页数：10

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