@article {36612,
	title = {A novel quaternion linear matrix equation solver through zeroing neural networks with applications to acoustic source tracking},
	journal = {AIMS Mathematics},
	volume = {8},
	number = {11},
	year = {2023},
	pages = {25966-25989},
	abstract = {Due to its significance in science and engineering, time-varying linear matrix equation (LME) problems have received a lot of attention from scholars. It is for this reason that the issue of finding the minimum-norm least-squares solution of the time-varying quaternion LME (ML-TQ-LME) is addressed in this study. This is accomplished using the zeroing neural network (ZNN) technique, which has achieved considerable success in tackling time-varying issues. In light of that, two new ZNN models are introduced to solve the ML-TQ-LME problem for time-varying quaternion matrices of arbitrary dimension. Two simulation experiments and two practical acoustic source tracking applications show that the models function superbly.},
	keywords = {acoustic source tracking, dynamical system, linear matrix equation, minimum-norm least-squares solution, quaternion, Zeroing neural network},
	issn = {2473-6988},
	doi = {10.3934/math.20231323},
	url = {https://www.aimspress.com/article/doi/10.3934/math.20231323},
	author = {Kovalnogov, Vladislav N. and Fedorov, Ruslan V. and Igor I. Shepelev and Vyacheslav V. Sherkunov and Theodore E. Simos and Spyridon D. Mourtas and Vasilios N. Katsikis}
}
