TY  - JOUR
AU  - Randjelović, Branislav M.
AU  - Mitić, Vojislav V.
AU  - Ribar, Srđan
AU  - Milošević, Dušan M.
AU  - Lazović, Goran
AU  - Fecht, Hans J.
AU  - Vlahović, Branislav
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3773
AB  - Many recently published research papers examine the representation of nanostructures and biomimetic materials, especially using mathematical methods. For this purpose, it is important that the mathematical method is simple and powerful. Theory of fractals, artificial neural networks and graph theory are most commonly used in such papers. These methods are useful tools for applying mathematics in nanostructures, especially given the diversity of the methods, as well as their compatibility and complementarity. The purpose of this paper is to provide an overview of existing results in the field of electrochemical and magnetic nanostructures parameter modeling by applying the three methods that are "easy to use": theory of fractals, artificial neural networks and graph theory. We also give some new conclusions about applicability, advantages and disadvantages in various different circumstances.
PB  - MDPI, Basel
T2  - Fractal and Fractional
T1  - Fractal Nature Bridge between Neural Networks and Graph Theory Approach within Material Structure Characterization
IS  - 3
VL  - 6
DO  - 10.3390/fractalfract6030134
ER  - 

@article{
author = "Randjelović, Branislav M. and Mitić, Vojislav V. and Ribar, Srđan and Milošević, Dušan M. and Lazović, Goran and Fecht, Hans J. and Vlahović, Branislav",
year = "2022",
abstract = "Many recently published research papers examine the representation of nanostructures and biomimetic materials, especially using mathematical methods. For this purpose, it is important that the mathematical method is simple and powerful. Theory of fractals, artificial neural networks and graph theory are most commonly used in such papers. These methods are useful tools for applying mathematics in nanostructures, especially given the diversity of the methods, as well as their compatibility and complementarity. The purpose of this paper is to provide an overview of existing results in the field of electrochemical and magnetic nanostructures parameter modeling by applying the three methods that are "easy to use": theory of fractals, artificial neural networks and graph theory. We also give some new conclusions about applicability, advantages and disadvantages in various different circumstances.",
publisher = "MDPI, Basel",
journal = "Fractal and Fractional",
title = "Fractal Nature Bridge between Neural Networks and Graph Theory Approach within Material Structure Characterization",
number = "3",
volume = "6",
doi = "10.3390/fractalfract6030134"
}

Randjelović, B. M., Mitić, V. V., Ribar, S., Milošević, D. M., Lazović, G., Fecht, H. J.,& Vlahović, B.. (2022). Fractal Nature Bridge between Neural Networks and Graph Theory Approach within Material Structure Characterization. in Fractal and Fractional
MDPI, Basel., 6(3).
https://doi.org/10.3390/fractalfract6030134

Randjelović BM, Mitić VV, Ribar S, Milošević DM, Lazović G, Fecht HJ, Vlahović B. Fractal Nature Bridge between Neural Networks and Graph Theory Approach within Material Structure Characterization. in Fractal and Fractional. 2022;6(3).
doi:10.3390/fractalfract6030134 .

Randjelović, Branislav M., Mitić, Vojislav V., Ribar, Srđan, Milošević, Dušan M., Lazović, Goran, Fecht, Hans J., Vlahović, Branislav, "Fractal Nature Bridge between Neural Networks and Graph Theory Approach within Material Structure Characterization" in Fractal and Fractional, 6, no. 3 (2022),
https://doi.org/10.3390/fractalfract6030134 . .