H∞ state estimation of static delayed neural networks with non-fragile sampled-data control

被引：0

作者：

Liu Y. ^{[1
]}

Lee S. ^{[1
]}

机构：

[1] Dept. of Electronic Engineering, Kyungpook National University

来源：

Transactions of the Korean Institute of Electrical Engineers | 2017年 / 66卷 / 01期

基金：

新加坡国家研究基金会;

关键词：

Neural networks; Non-fragile sampled-data control; State estimation; Time-varying delay;

D O I：

10.5370/KIEE.2017.66.1.171

中图分类号：

学科分类号：

摘要：

This paper studies the state estimation problem for static neural networks with time-varying delay. Unlike other studies, the controller scheme, which involves time-varying sampling and uncertainties, is first employed to design the state estimator for delayed static neural networks. Based on Lyapunov functional approach and linear matrix inequality technique, the non-fragile sampled-data estimator is designed such that the resulting estimation error system is globally asymptotically stable with H∞ performance. Finally, the effectiveness of the developed results is demonstrated by a numerical example. Copyright © The Korean Institute of Electrical Engineers.

引用

页码：171 / 178

页数：7

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