https://doi.org/10.1140/epjd/s10053-021-00188-3
Regular Article - Optical Phenomena and Photonics
Pitchfork and Hopf bifurcations in quantum dot light emitting diode: Analysis and prediction by using artificial neural network
1
Department of Physics, College of Education, Nyala University, P.O. Box: 155, Nyala, Sudan
2
Institut de Mathématiques et de Sciences Physiques, Université d’Abomey-Calavi, B.P. 613, Porto Novo, Benin
3
Department of Electronic and Automation, Vocational School of Hacıbektaş, Nevşehir Hacı Bektaş Veli University, 50800, Hacıbektaş, Nevşehir, Turkey
4
Department of Mechanical, Petroleum and Gas Engineering, Faculty of Mines and Petroleum Industries, University of Maroua, P.O. Box 46, Maroua, Cameroon
5
Center for Nonlinear Systems, Chennai Institute of Technology, 600069, Chennai, India
6
School of Mathematics and Physics, China University of Geosciences, 430074, Wuhan, China
Received:
28
December
2020
Accepted:
27
May
2021
Published online:
2
June
2021
The analytical and numerical analyses as well as prediction with artificial neural network (ANN) for chaos-based artificial intelligence applications of quantum dot light emitting diode (QDLED) are investigated in this paper. The system of equations describing QDLED has three, or one equilibrium points depending on the capture rate from wetting layer into the dot and the injection current. The stability analysis of the equilibrium points reveals the existence of Pitchfork and Hopf bifurcations. The different dynamical behaviors (including steady state, periodic and chaotic behaviors) found in QDLED are illustrated in two parameters bifurcation diagrams, phase portraits and time series. Finaly, the QDLED system is predicted using ANN for chaos-based artificial intelligence applications.
© The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2021
