Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)Gutiérrez Salamanca, Rafael MaríaHernández Duarte, Andrés IgnacioCruz Cardozo, José Laureano2023-08-012023-08-012023-05-29http://repositorio.uan.edu.co/handle/123456789/8353This work presents a procedure for constructing models of nonlinear dynamic systems that exhibit deterministic chaos behaviors based on time series data. The procedure involves extracting information from the time series to reconstruct the dynamic system in phase space, using second-order equations and three variables. We implemented this procedure in Python as a software and tested it on the well-known Lorenz system, extensively studied in chaos theory.En este trabajo se presenta un procedimiento para construir, a partir de series de tiempo, modelos de sistemas dinámicos no lineales que exhiben comportamientos de caos determinista. El procedimiento consiste en extraer información contenida en la serie de tiempo para reconstruir el sistema dinámico en el espacio de fase, utilizando ecuaciones de segundo orden y tres variables. Este procedimiento fue implementado en un software programado en Python y probado con el conocido sistema de Lorenz, el cual ha sido ampliamente estudiado en la teoría del caos.spaAcceso abiertoDoctorado en Ciencia Aplicada51014.23 C957sTesis y disertaciones (Maestría y/o Doctorado)Doctorate in Applied Sciencesinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Abarbanel, H. (1996). Analysis of observed chaotic data. In Physics Today (Vol. 39). https://doi.org/10.2307/1271140Abdelkareem, M. A., Elsaid, K., Wilberforce, T., Kamil, M., Sayed, E. T., & Olabi, A. (2021). Environmental aspects of fuel cells: {A} review. Science of The Total Environment, 752, 141803. https://doi.org/10.1016/j.scitotenv.2020.141803Barbir, F. (2013). {PEM} fuel cells: theory and practice (2nd ed). Elsevier/Academic Press.Benchouia, N. E., Derghal, A., Mahmah, B., Madi, B., Khochemane, L., & Hadjadj Aoul, E. (2015). An adaptive fuzzy logic controller (AFLC) for PEMFC fuel cell. International Journal of Hydrogen Energy, 40(39), 13806–13819. https://doi.org/10.1016/J.IJHYDENE.2015.05.189Bressel, M., Ould Bouamama, B., Hissel, D., & Hilairet, M. (2015). A {Review} on {Graphical} {Methods} for {Modeling} a {Proton} {Exchange} {Membrane} {Fuel} {Cell}. Journal of Fuel Cell Science and Technology, 12(6), 60801. https://doi.org/10.1115/1.4032336Brown, R., Rulkov, N. F., & Tracy, E. R. (1994). Modeling and synchronizing chaotic systems from time-series data. In Physical Review E (Vol. 49, Issue 5, pp. 3784–3800). https://doi.org/10.1103/PhysRevE.49.3784Burkholder, M. B. (2015). Nonlinear Analysis, Control, and Modeling of the Two-Phase Flow Dynamics in Polymer Electrolyte Fuel Cells.Burkholder, M. B., Siefert, N. S., & Litster, S. (2014). Nonlinear analysis of voltage dynamics in a polymer electrolyte fuel cell due to two-phase channel flow. Journal of Power Sources, 267, 243–254. https://doi.org/10.1016/j.jpowsour.2014.04.156Cruz, J. L., Gutiérrez, R. M., & Pastrán, C. G. (2021). Software to build dynamical systems models from time series with chaotic behavior. Journal of Physics: Conference Series, 1938(1). https://doi.org/10.1088/1742-6596/1938/1/012023Fan, Z., Dong, S., Chi, J., Zhuang, X., & Mastorakis, N. E. (2018). A fast algorithm of correlation dimension estimation for nonlinear time series. Proceedings - 2018 2nd European Conference on Electrical Engineering and Computer Science, EECS 2018, 595–597. https://doi.org/10.1109/EECS.2018.00115instname:Universidad Antonio Nariñoreponame:Repositorio Institucional UANrepourl:https://repositorio.uan.edu.co/