@article {STANIMIROVIC2024128412,
	title = {Zeroing neural network approaches for computing time-varying minimal rank outer inverse},
	journal = {Applied Mathematics and Computation},
	volume = {465},
	year = {2024},
	pages = {128412},
	abstract = {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.},
	keywords = {Dynamic system, Generalized inverse, Matrix equation, Minimal rank outer inverse, Zeroing neural network},
	issn = {0096-3003},
	doi = {https://doi.org/10.1016/j.amc.2023.128412},
	url = {https://www.sciencedirect.com/science/article/pii/S0096300323005817},
	author = {Predrag S. Stanimirovi{\'c} and Spyridon D. Mourtas and Mosi{\'c}, Dijana and Vasilios N. Katsikis and Cao, Xinwei and Shuai Li}
}
