Finite-time non-fragile state estimation for discrete neural networks with sensor failures, time-varying delays and randomly occurring sensor nonlinearity

被引：15

作者：

Li, Jian-Ning ^{[1
]}

Xu, Yu-Fei ^{[1
]}

Bao, Wen-Dong ^{[1
]}

Li, Zhu-Jian ^{[1
]}

Li, Lin-Sheng ^{[2
]}

机构：

[1] Hangzhou Dianzi Univ, Inst Syst Sci & Control Engn, Sch Automat, Hangzhou 310018, Zhejiang, Peoples R China

[2] Taiyuan Univ Sci & Technol, Coll Elect Informat & Engn, Taiyuan 030024, Shanxi, Peoples R China

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2019年 / 356卷 / 03期

基金：

中国国家自然科学基金;

关键词：

GLOBAL ASYMPTOTIC STABILITY; JUMP SYSTEMS;

D O I：

10.1016/j.jfranklin.2018.10.032

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A finite-time non-fragile state estimation algorithm is discussed in this article for discrete delayed neural networks with sensor failures and randomly occurring sensor nonlinearity. First, by using augmented technology, such system is modeled as a kind of nonlinear stochastic singular delayed system. Then, a finite-time state estimator algorithm is provided to ensure that the singular error dynamic is regular, causal and stochastic finite-time stable. Moreover, the states and sensor failures can be estimated simultaneously. Next, in order to avoid the affection of estimator's parameter perturbation, a finite-time non-fragile state estimation algorithm is given, and a simulation result demonstrates the usefulness of the proposed approach. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

引用

页码：1566 / 1589

页数：24

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