A Zeroing Neural Network Approach for Calculating Time-Varying G-Outer Inverse of Arbitrary Matrix

被引：0
作者：
Stanimirovic, Predrag S. [1 ,2 ]
Mourtas, Spyridon D. [2 ,3 ]
Mosic, Dijana [1 ]
Katsikis, Vasilios N. [3 ]
Cao, Xinwei [4 ]
Li, Shuai [5 ]
机构：
[1] Univ Nis, Fac Sci & Math, Nish 18000, Serbia
[2] Siberian Fed Univ, Lab Hybrid Methods Modeling & Optimizat Complex S, Krasnoyarsk 660041, Russia
[3] Natl & Kapodistrian Univ Athens, Dept Econ, Div Math Informat & Stat Econometr, Athens 10559, Greece
[4] Jiangnan Univ, Sch Business, Wuxi 214122, Peoples R China
[5] Univ Oulu, Fac Informat Technol, Oulu 90570, Finland
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2025年 / 36卷 / 05期
关键词：
Mathematical models; TV; Dynamical systems; Computational modeling; Nickel; Location awareness; Numerical models; Dynamic system; generalized inverse; generalized-outer (G-outer) inverse; zeroing neural network (ZNN); MOORE-PENROSE INVERSE; SYSTEMS; DESIGN; PSEUDOINVERSION; ALGORITHM; EQUATIONS;
D O I：
10.1109/TNNLS.2024.3415717
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Calculation of the time-varying (TV) matrix generalized inverse has grown into an essential tool in many fields, such as computer science, physics, engineering, and mathematics, in order to tackle TV challenges. This work investigates the challenge of finding a TV extension of a subclass of inner inverses on real matrices, known as generalized-outer (G-outer) inverses. More precisely, our goal is to construct TV G-outer inverses (TV-GOIs) by utilizing the zeroing neural network (ZNN) process, which is presently thought to be a state-of-the-art solution to tackling TV matrix challenges. Using known advantages of ZNN dynamic systems, a novel ZNN model, called ZNNGOI, is presented in the literature for the first time in order to compute TV-GOIs. The ZNNGOI performs excellently in performed numerical simulations and an application on addressing localization problems. In terms of solving linear TV matrix equations, its performance is comparable to that of the standard ZNN model for computing the pseudoinverse.
引用
收藏
页码：8843 / 8852
页数：10
相关论文
共 50 条
[41]   Zhang neural network solving for time-varying full-rank matrix Moore-Penrose inverse [J].
Zhang, Yunong ;
Yang, Yiwen ;
Tan, Ning ;
Cai, Binghuang .
COMPUTING, 2011, 92 (02) :97-121
[42]   Noise-suppressing zeroing neural network for online solving time-varying matrix square roots problems: A control-theoretic approach [J].
Sun, Zhongbo ;
Wang, Gang ;
Jin, Long ;
Cheng, Chao ;
Zhang, Bangcheng ;
Yu, Junzhi .
EXPERT SYSTEMS WITH APPLICATIONS, 2022, 192
[43]   Complex Neural Network Models for Time-Varying Drazin Inverse [J].
Wang, Xue-Zhong ;
Wei, Yimin ;
Stanimirovic, Predrag S. .
NEURAL COMPUTATION, 2016, 28 (12) :2790-2824
[44]   Design and Validation of Zeroing Neural Network to Solve Time-Varying Algebraic Riccati Equation [J].
Liu, Hang ;
Wang, Tie ;
Guo, Dongsheng .
IEEE ACCESS, 2020, 8 :211315-211323
[45]   Inverse-Free Hybrid Spatial-Temporal Derivative Neural Network for Time-Varying Matrix Moore-Penrose Inverse and Its Circuit Schematic [J].
Zhang, Bing ;
Zheng, Yuhua ;
Li, Shuai ;
Chen, Xinglong ;
Mao, Yao ;
Pham, Duc Truong .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2025, 72 (03) :499-503
[46]   Finite-time convergent zeroing neural network for solving time-varying algebraic Riccati equations [J].
Simos, Theodore E. ;
Katsikis, Vasilios N. ;
Mourtas, Spyridon D. ;
Stanimirovic, Predrag S. .
JOURNAL OF THE FRANKLIN INSTITUTE, 2022, 359 (18) :10867-10883
[47]   Solving Time-Varying Nonsymmetric Algebraic Riccati Equations With Zeroing Neural Dynamics [J].
Simos, Theodore E. E. ;
Katsikis, Vasilios N. N. ;
Mourtas, Spyridon D. D. ;
Stanimirovic, Predrag S. S. .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 53 (10) :6575-6587
[48]   Towards Higher-Order Zeroing Neural Network Dynamics for Solving Time-Varying Algebraic Riccati Equations [J].
Jerbi, Houssem ;
Alharbi, Hadeel ;
Omri, Mohamed ;
Ladhar, Lotfi ;
Simos, Theodore E. ;
Mourtas, Spyridon D. ;
Katsikis, Vasilios N. .
MATHEMATICS, 2022, 10 (23)
[49]   A novel discrete zeroing neural network for online solving time-varying nonlinear optimization problems [J].
Song, Feifan ;
Zhou, Yanpeng ;
Xu, Changxian ;
Sun, Zhongbo .
FRONTIERS IN NEUROROBOTICS, 2024, 18
[50]   An Adaptive Zeroing Neural Network with Non-Convex Activation for Time-Varying Quadratic Minimization [J].
Yi, Hang ;
Peng, Wenjun ;
Xiao, Xiuchun ;
Feng, Shaojin ;
Zhu, Hengde ;
Zhang, Yudong .
MATHEMATICS, 2023, 11 (11)
12345