Applications of artificial neural networks in physics
Mahmoud. Y. El-Bakry; D. M. Habashy; Aamer, Engy; R. A. Mohamed;
Abstract
In this research, the artificial neural network (ANN) and resilient back propagation (R-prop) training algorithm are utilized to model the photovoltaic properties of Nickel–phthalocyanine (NiPc/p-Si) heterojunction. The experimental data are extracted from experimental studies. Experimental data are utilized as inputs in the ANN model Training of different structures of the ANN is processed to approach the minimum value of error. Eight
The ANN performances are also investigated and their values are very small (MSE < 10-3).the simulation results of the current-voltage characteristics of NiPc films are produced and provided excellent
matching with the corresponding experimental data. Utilization of ANN model for pred ictions is also processed
and gives accurate results. The equation which describes the relation between the inputs and outputs is
obtained. The high accuracy of the ANN model has appeared in the major guessing power and the ability
The ANN performances are also investigated and their values are very small (MSE < 10-3).the simulation results of the current-voltage characteristics of NiPc films are produced and provided excellent
matching with the corresponding experimental data. Utilization of ANN model for pred ictions is also processed
and gives accurate results. The equation which describes the relation between the inputs and outputs is
obtained. The high accuracy of the ANN model has appeared in the major guessing power and the ability
Other data
Title | Applications of artificial neural networks in physics | Authors | Mahmoud. Y. El-Bakry; D. M. Habashy; Aamer, Engy ; R. A. Mohamed | Keywords | (Organic/Inorganic) Heterojunction | Issue Date | 4-Apr-2020 | Publisher | R. A. Mohamed | Journal | Journal of Advances in Physics | Volume | 17 | Start page | 306 | End page | 321 | DOI | https://doi.org/10.24297/jap.v17i.8718 |
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