@article {32359,
	title = {Zeroing Neural Network Based on Neutrosophic Logic for Calculating Minimal-Norm Least-Squares Solutions to Time-Varying Linear Systems},
	journal = {Neural Processing Letters},
	year = {2023},
	month = {2023},
	abstract = {This paper presents a dynamic model based on neutrosophic numbers and a neutrosophic logic engine. The introduced neutrosophic logic/fuzzy adaptive Zeroing Neural Network dynamic is termed NSFZNN and represents an improvement over the traditional Zeroing Neural Network (ZNN) design. The model aims to calculate the matrix pseudo-inverse and the minimum-norm least-squares solutions of time-varying linear systems. The improvement of the proposed model emerges from the advantages of neutrosophic logic over fuzzy and intuitionistic fuzzy logic in solving complex problems associated with predictions, vagueness, uncertainty, and imprecision. We use neutrosphication, de-fuzzification, and de-neutrosophication instead of fuzzification and de-fuzzification exploited so far. The basic idea is based on the known advantages of neutrosophic systems compared to fuzzy systems. Simulation examples and engineering applications on localization problems and electrical networks are presented to test the efficiency and accuracy of the proposed dynamical system.},
	isbn = {1573-773X},
	url = {https://doi.org/10.1007/s11063-023-11175-7},
	author = {Vasilios N. Katsikis and Predrag S. Stanimirovi{\'c} and Spyridon D. Mourtas and Xiao, Lin and Stanujki{\'c}, Dragi{\v s}a and Karaba{\v s}evi{\'c}, Darjan}
}
