@article {math10111950,
	title = {Zeroing Neural Network Approaches Based on Direct and Indirect Methods for Solving the Yang\&ndash;Baxter-like Matrix Equation},
	journal = {Mathematics},
	volume = {10},
	number = {11},
	year = {2022},
	abstract = {This research introduces three novel zeroing neural network (ZNN) models for addressing the time-varying Yang\&ndash;Baxter-like matrix equation (TV-YBLME) with arbitrary (regular or singular) real time-varying (TV) input matrices in continuous time. One ZNN dynamic utilizes error matrices directly arising from the equation involved in the TV-YBLME. Moreover, two ZNN models are proposed using basic properties of the YBLME, such as the splitting of the YBLME and sufficient conditions for a matrix to solve the YBLME. The Tikhonov regularization principle enables addressing the TV-YBLME with an arbitrary input real TV matrix. Numerical experiments, including nonsingular and singular TV input matrices, show that the suggested models deal effectively with the TV-YBLME.},
	issn = {2227-7390},
	doi = {10.3390/math10111950},
	url = {https://www.mdpi.com/2227-7390/10/11/1950},
	author = {Jiang, Wendong and Lin, Chia-Liang and Vasilios N. Katsikis and Spyridon D. Mourtas and Predrag S. Stanimirovi{\'c} and Theodore E. Simos}
}
