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

共 50 条

[1] Novel fixed-time zeroing neural network models for solving time-varying minimal rank outer inverse problem
Miao, Peng
Huang, Huihui
Communications in Nonlinear Science and Numerical Simulation, 2025, 142
[2] A Zeroing Neural Network Approach for Calculating Time-Varying G-Outer Inverse of Arbitrary Matrix
Stanimirovic, Predrag S.
Mourtas, Spyridon D.
Mosic, Dijana
Katsikis, Vasilios N.
Cao, Xinwei
Li, Shuai
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, : 1 - 10
[3] An Efficient Anti-Noise Zeroing Neural Network for Time-Varying Matrix Inverse
Hu, Jiaxin
Yang, Feixiang
Huang, Yun
AXIOMS, 2024, 13 (08)
[4] Terminal zeroing neural network for time-varying matrix computing under bounded noise
Zhong, Guomin
Tang, Yifei
Sun, Mingxuan
Tongxin Xuebao/Journal on Communications, 2024, 45 (09): : 55 - 67
[5] An efficient zeroing neural network for solving time-varying nonlinear equations
Behera, Ratikanta
Gerontitis, Dimitris
Stanimirovic, Predrag
Katsikis, Vasilios
Shi, Yang
Cao, Xinwei
NEURAL COMPUTING & APPLICATIONS, 2023, 35 (24): : 17537 - 17554
[6] An efficient zeroing neural network for solving time-varying nonlinear equations
Ratikanta Behera
Dimitris Gerontitis
Predrag Stanimirović
Vasilios Katsikis
Yang Shi
Xinwei Cao
Neural Computing and Applications, 2023, 35 : 17537 - 17554
[7] Improved finite-time zeroing neural network for time-varying division
Gerontitis, Dimitris
Behera, Ratikanta
Sahoo, Jajati Keshari
Stanimirovic, Predrag S.
STUDIES IN APPLIED MATHEMATICS, 2021, 146 (02) : 526 - 549
[8] Time-Varying Pseudoinversion Based on Full-Rank Decomposition and Zeroing Neural Networks
Alharbi, Hadeel
Jerbi, Houssem
Kchaou, Mourad
Abbassi, Rabeh
Simos, Theodore E.
Mourtas, Spyridon D.
Katsikis, Vasilios N.
MATHEMATICS, 2023, 11 (03)
[9] Double integral-enhanced Zeroing neural network with linear noise rejection for time-varying matrix inverse
Liao, Bolin
Han, Luyang
Cao, Xinwei
Li, Shuai
Li, Jianfeng
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY, 2024, 9 (01) : 197 - 210
[10] Zeroing neural network for time-varying convex quadratic programming with linear noise
Li J.
Liu Z.
Rong Y.
Li Z.
Liao B.
Qu L.
Liu Z.
Lin K.
Tongxin Xuebao/Journal on Communications, 2023, 44 (04): : 226 - 233

← 1 2 3 4 5 →