Observer-based state estimation for memristive neural networks with time-varying delay

被引：20

作者：

Guo, Moxuan ^{[1
]}

Zhu, Song ^{[1
]}

Liu, Xiaoyang ^{[2
]}

机构：

[1] China Univ Min & Technol, Sch Math, Xuzhou 221116, Peoples R China

[2] Jiangsu Normal Univ, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China

来源：

KNOWLEDGE-BASED SYSTEMS | 2022年 / 246卷

基金：

中国国家自然科学基金;

关键词：

Memristive neural networks; State estimation; State observer; Linear matrix inequality; H-INFINITY; UNCERTAIN SYSTEMS; STABILITY; DISCRETE;

D O I：

10.1016/j.knosys.2022.108707

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 investigates the observer-based state estimation of memristive neural networks (MNNs) with time-varying delay. In order to obtain the accurate state information of the switching MNNs system, a new full-order state observer based on system measurement output is developed such that the reconstruction of states is achieved. By applying set-valued maps and differential inclusions, some sufficient conditions for delay-independent and delay-dependent asymptotic stability are obtained. The validity of theoretical results is verified via numerical examples. (C)2022 Elsevier B.V. All rights reserved.

引用

页数：8

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