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

被引：6
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
相关论文
共 50 条
  • [31] Zeroing Neural Network for Pseudoinversion of an Arbitrary Time-Varying Matrix Based on Singular Value Decomposition
    Kornilova, Mariya
    Kovalnogov, Vladislav
    Fedorov, Ruslan
    Zamaleev, Mansur
    Katsikis, Vasilios N.
    Mourtas, Spyridon D.
    Simos, Theodore E.
    [J]. MATHEMATICS, 2022, 10 (08)
  • [32] Unified Model Solving Nine Types of Time-Varying Problems in the Frame of Zeroing Neural Network
    Li, Jian
    Shi, Yang
    Xuan, Hejun
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 32 (05) : 1896 - 1905
  • [33] A novel discrete zeroing neural network for online solving time-varying nonlinear optimization problems
    Song, Feifan
    Zhou, Yanpeng
    Xu, Changxian
    Sun, Zhongbo
    [J]. FRONTIERS IN NEUROROBOTICS, 2024, 18
  • [34] Accelerating noise-tolerant zeroing neural network with fixed-time convergence to solve the time-varying Sylvester equation
    Zhang, Miaomiao
    Zheng, Bing
    [J]. AUTOMATICA, 2022, 135
  • [35] Efficient Predefined-Time Adaptive Neural Networks for Computing Time-Varying Tensor Moore–Penrose Inverse
    Qi, Zhaohui
    Ning, Yingqiang
    Xiao, Lin
    Wang, Zidong
    He, Yongjun
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, : 3659 - 3670
  • [36] Two neural dynamics approaches for computing system of time-varying nonlinear equations
    Xiao, Xiuchun
    Fu, Dongyang
    Wang, Guancheng
    Liao, Shan
    Qi, Yimeng
    Huang, Haoen
    Jin, Long
    [J]. NEUROCOMPUTING, 2020, 394 : 84 - 94
  • [37] A High Order Integration Enhanced Zeroing Neural Network for Time-Varying Convex Quadratic Program Against Nonlinear Noise
    Li, Jianfeng
    Rong, Yang
    Li, Zhan
    Li, Shuai
    Qu, Linxi
    [J]. IEEE ACCESS, 2024, 12 : 22098 - 22109
  • [38] A higher-order zeroing neural network for pseudoinversion of an arbitrary time-varying matrix with applications to mobile object localization
    Simos, Theodore E.
    Katsikis, Vasilios N.
    Mourtas, Spyridon D.
    Stanimirovic, Predrag S.
    Gerontitis, Dimitris
    [J]. INFORMATION SCIENCES, 2022, 600 : 226 - 238
  • [39] A Novel Zeroing Neural Network for Solving Time-Varying Quadratic Matrix Equations against Linear Noises
    Li, Jianfeng
    Qu, Linxi
    Li, Zhan
    Liao, Bolin
    Li, Shuai
    Rong, Yang
    Liu, Zheyu
    Liu, Zhijie
    Lin, Kunhuang
    [J]. MATHEMATICS, 2023, 11 (02)
  • [40] A Neural Network for Moore-Penrose Inverse of Time-Varying Complex-Valued Matrices
    Chai, Yiyuan
    Li, Haojin
    Qiao, Defeng
    Qin, Sitian
    Feng, Jiqiang
    [J]. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2020, 13 (01) : 663 - 671
12345