Fractal Nature Bridge between Neural Networks and Graph Theory Approach within Material Structure Characterization
2022
Аутори
Randjelović, Branislav M.Mitić, Vojislav V.
Ribar, Srđan
Milošević, Dušan M.
Lazović, Goran
Fecht, Hans J.
Vlahović, Branislav
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Кључне речи:
materials / graph theory / fractals / artificial neural networksИзвор:
Fractal and Fractional, 2022, 6, 3Издавач:
- MDPI, Basel
Финансирање / пројекти:
- North Carolina Central University, Durham (USA)
- Institute of Functional Nanosystems, Ulm (Germany)
DOI: 10.3390/fractalfract6030134
ISSN: 2504-3110
WoS: 000776500800001
Scopus: 2-s2.0-85125949142
Колекције
Институција/група
Mašinski fakultetTY - 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 . .