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
收藏
页数：16
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