State bounding for fuzzy memristive neural networks with bounded input disturbances

被引：15
作者：
Gao, Yu [1 ]
Zhu, Song [1 ]
Yang, Chunyu [2 ]
Wen, Shiping [3 ]
机构：
[1] China Univ Min & Technol, Sch Math, Xuzhou 221116, Jiangsu, Peoples R China
[2] China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Jiangsu, Peoples R China
[3] Univ Technol Sydney, Ctr Artificial Intelligence, Sydney, NSW 2007, Australia
来源：
NEURAL NETWORKS | 2021年 / 134卷
基金：
中国国家自然科学基金;
关键词：
Memristor; Neural networks; Fuzzy systems; State bounding; Bounded disturbances; FINITE-TIME SYNCHRONIZATION; LINEAR-SYSTEMS; REACHABLE SETS; EXPONENTIAL STABILIZATION; SWITCHING SYSTEMS; INVARIANT-SETS; DELAY SYSTEMS; DISCRETE; STABILITY; PASSIVITY;
D O I：
10.1016/j.neunet.2020.11.016
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 state bounding problem of fuzzy memristive neural networks (FMNNs) with bounded input disturbances. By using the characters of Metzler, Hurwitz and nonnegative matrices, this paper obtains the exact delay-independent and delay-dependent boundary ranges of the solution, which have less conservatism than the results in existing literatures. The validity of the results is verified by two numerical examples. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页码：163 / 172
页数：10
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