Robust state estimation for fractional-order complex-valued delayed neural networks with interval parameter uncertainties: LMI approach

被引：45
|
作者：
Hu, Binxin [1 ]
Song, Qiankun [1 ,2 ]
Zhao, Zhenjiang [3 ]
机构：
[1] Chongqing Jiaotong Univ, Dept Math, Chongqing 400074, Peoples R China
[2] Chongqing Jiaotong Univ, State Key Lab Mt Bridge & Tunnel Engn, Chongqing 400074, Peoples R China
[3] Huzhou Univ, Dept Math, Huzhou 313000, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2020年 / 373卷
基金：
中国国家自然科学基金;
关键词：
State estimation; Fractional-order; Complex-valued neural networks; Interval parameter uncertainty; Time delay; STABILITY ANALYSIS; LYAPUNOV FUNCTIONS; GLOBAL STABILITY; DYNAMICS; SYSTEMS; MODEL;
D O I：
10.1016/j.amc.2020.125033
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Without separating complex-valued neural networks into two real-valued systems, the state estimation of fractional-order complex-valued neural networks (FCNNs) with uncertain parameters and time delay is investigated in this paper. Based on Lyapunov-Krasovskii functional approach, a new linear matrix inequality (LMI) criterion is derived for asymptotic stability of the estimation error system. A numerical example with simulations is given to confirm the feasibility and availability of the raised result. (C) 2020 Elsevier Inc. All rights reserved.
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页数：12
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