@article {SIMOS2022226,
	title = {A higher-order zeroing neural network for pseudoinversion of an arbitrary time-varying matrix with applications to mobile object localization},
	journal = {Information Sciences},
	volume = {600},
	year = {2022},
	pages = {226-238},
	abstract = {The hyperpower family of iterative methods with arbitrary convergence order is one of the most used methods for estimating matrix inverses and generalized inverses, whereas the zeroing neural network (ZNN) is a type of neural dynamics developed to solve time-varying problems in science and engineering. Since the discretization of ZNN dynamics leads to the Newton iterative method for solving the matrix inversion and generalized inversion, this study proposes and investigates a family of ZNN dynamical models known as higher-order ZNN (HOZNN) models, which are defined on the basis of correlation with hyperpower iterations of arbitrary order. Because the HOZNN dynamical system requires error function powers, it is only applicable to square error functions. In this paper, we extend the original HOZNN dynamic flows to arbitrary time-dependent real matrices, both square and rectangular, and sign-bi-power activation is used to investigate the finite-time convergence of arbitrary order HOZNN dynamics. The proposed models are theoretically and numerically tested under three activation functions, and an application in solving the angle-of-arrival (AoA) localization problem demonstrates the effectiveness of the proposed design.},
	keywords = {dynamical system, Hyperpower iterations, Moore{\textendash}Penrose inverse, Zeroing neural network},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2022.03.094},
	url = {https://www.sciencedirect.com/science/article/pii/S0020025522003218},
	author = {Theodore E. Simos and Vasilios N. Katsikis and Spyridon D. Mourtas and Predrag S. Stanimirovi{\'c} and Dimitris Gerontitis}
}
