@article {JERBI2026117058,
	title = {An innovative neutrosophic logic adaptive high-order zeroing neural network for solving linear matrix equations: Applications to acoustic source tracking},
	journal = {Journal of Computational and Applied Mathematics},
	volume = {475},
	year = {2026},
	pages = {117058},
	abstract = {Scholars have put a lot of emphasis on time-varying linear matrix equations (LMEs) problems because of its importance in science and engineering. The problem of determining the time-varying LME{\textquoteright}s minimum-norm least-squares solution (MLLE) is therefore tackled in this work. This is achieved by the use of NHZNN, a recently developed neutrosophic logic/fuzzy adaptive high-order zeroing neural network technique. The NHZNN is an advancement on the conventional zeroing neural network (ZNN) technique, which has shown great promise in solving time-varying tasks. To address the MLLE task for arbitrary-dimensional time-varying matrices, three novel ZNN models are presented. The models perform exceptionally well, as demonstrated by two simulation studies and two real-world applications to acoustic source tracking.},
	keywords = {Acoustic source localization, Fuzzy system, linear matrix equation, minimum-norm least-squares solution, Neutrosophic logic, Zeroing neural network},
	issn = {0377-0427},
	doi = {https://doi.org/10.1016/j.cam.2025.117058},
	url = {https://www.sciencedirect.com/science/article/pii/S0377042725005722},
	author = {Jerbi, Houssem and Aoun, Sondess Ben and Abbassi, Rabeh and Kchaou, Mourad and Theodore E. Simos and Spyridon D. Mourtas and Vasilios N. Katsikis and Shuai Li and Cao, Xinwei}
}
