Zeroing neural network approaches for computing time-varying minimal rank outer inverse

被引：3

作者：

Stanimirovic, Predrag S. ^{[1
,3
]}

Mourtas, Spyridon D. ^{[2
,3
]}

Mosic, Dijana ^{[1
]}

Katsikis, Vasilios N. ^{[2
]}

Cao, Xinwei ^{[4
]}

Li, Shuai ^{[5
]}

机构：

[1] Univ Nis, Fac Sci & Math, Visegradska 33, Nish 18000, Serbia

[2] Natl & Kapodistrian Univ Athens, Dept Econ, Div Math Informat & Stat Econometr, Sofokleous 1 St, Athens 660041, Greece

[3] Siberian Fed Univ, Lab Hybrid Methods Modelling & Optimizat Complex S, Prosp Svobodny 79, Krasnoyarsk 660041, Russia

[4] Jiangnan Univ, Sch Business, Lihu Blvd, Wuxi 214122, Peoples R China

[5] Univ Oulu, Fac Informat Technol, Oulu, Finland

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2024年 / 465卷

关键词：

Matrix equation; Zeroing neural network; Generalized inverse; Dynamic system; Minimal rank outer inverse; MOORE-PENROSE INVERSE; ALGORITHM; DESIGN; MODEL;

D O I：

10.1016/j.amc.2023.128412

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Generalized inverses are extremely effective in many areas of mathematics and engineering. The zeroing neural network (ZNN) technique, which is currently recognized as the state-of-the-art approach for calculating the time-varying Moore-Penrose matrix inverse, is investigated in this study as a solution to the problem of calculating the time-varying minimum rank outer inverse (TV-MROI) with prescribed range and/or TV-MROI with prescribed kernel. As a result, four novel ZNN models are introduced for computing the TV-MROI, and their efficiency is examined. Numerical tests examine and validate the effectiveness of the introduced ZNN models for calculating TV-MROI with prescribed range and/or prescribed kernel.

引用

页数：16

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