Identification of parameters in ordinary differential equation systems using artificial neural networks

The identification of parameters in systems of differential equations has been a complex scientific challenge, with limited traditional methods for modeling nonlinear physical phenomena and inconsistent experimental data. The objective was to compare an artificial neural network trained with Backpro...

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Κύριος συγγραφέας: Duque Aldaz, Francisco Javier (author)
Άλλοι συγγραφείς: Rodríguez-Flores, Fernando Raúl (author), Carmona Tapia, José (author)
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Έκδοση: 2025
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Διαθέσιμο Online:https://revista.sangregorio.edu.ec/index.php/REVISTASANGREGORIO/article/view/2826
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author Duque Aldaz, Francisco Javier
author2 Rodríguez-Flores, Fernando Raúl
Carmona Tapia, José
Duque Aldaz, Francisco Javier
author2_role author
author
author
author_facet Duque Aldaz, Francisco Javier
Rodríguez-Flores, Fernando Raúl
Carmona Tapia, José
Duque Aldaz, Francisco Javier
author_role author
collection Revista Universidad San Gregorio de Portoviejo
dc.creator.none.fl_str_mv Duque Aldaz, Francisco Javier
Rodríguez-Flores, Fernando Raúl
Carmona Tapia, José
Duque Aldaz, Francisco Javier
dc.date.none.fl_str_mv 2025-02-15
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv https://revista.sangregorio.edu.ec/index.php/REVISTASANGREGORIO/article/view/2826
10.36097/rsan.v1iEspecial_2.2826
dc.language.none.fl_str_mv spa
dc.publisher.none.fl_str_mv Universidad San Gregorio de Portoviejo
dc.relation.none.fl_str_mv https://revista.sangregorio.edu.ec/index.php/REVISTASANGREGORIO/article/view/2826/1730
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
dc.source.none.fl_str_mv Revista San Gregorio; Vol. 1 No. Especial_2 (2025): Revista San Gregorio; 15-23
Revista San Gregorio; Vol. 1 Núm. Especial_2 (2025): Revista San Gregorio; 15-23
2528-7907
1390-7247
10.36097/rsan.v1iEspecial_2
reponame:Revista Universidad San Gregorio de Portoviejo
instname:Universidad San Gregorio de Portoviejo
instacron:USGP
dc.subject.none.fl_str_mv Numerical methods
parameter tuning
artificial neural networks
Backpropagation algorithm
Levenberg-Marquardt method
Métodos numéricos
ajuste de parámetro
redes neuronales artificiales
algoritmo Backpropagation
método Levenberg-Marquardt
dc.title.none.fl_str_mv Identification of parameters in ordinary differential equation systems using artificial neural networks
Identificación de parámetros en sistemas ecuaciones diferenciales ordinarias mediante el uso de redes neuronales artificiales
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Artículo evaluado por pares
description The identification of parameters in systems of differential equations has been a complex scientific challenge, with limited traditional methods for modeling nonlinear physical phenomena and inconsistent experimental data. The objective was to compare an artificial neural network trained with Backpropagation and optimized by Levenberg-Marquardt against classical numerical methods to identify parameters in ordinary differential equations. A multilayer neural network was designed with one input, a hidden layer of 10 neurons and two outputs. The model was trained with experimental data divided into training, validation and test sets, using the Levenberg-Marquardt algorithm to fit its parameters. Accuracy was evaluated by comparing with the Runge-Kutta-based numerical method ODE45. The neural network demonstrated superior performance, achieving an accurate and less computationally complex approximation. While the ODE45 method presented good overall fits, it showed limitations at specific intervals due to spikes and discontinuities in the simulated functions. The neural network exhibited robustness in handling nonlinear dynamics, predicting with high accuracy the behavior of the system without requiring an explicit mathematical model. Its ability to recognize complex patterns with tolerable error margins consolidated it as an effective tool for dynamic systems. In conclusion, artificial neural networks were confirmed as a robust methodological alternative, allowing the modeling of dynamic nonlinear systems with simplicity, flexibility and scalability potential.
eu_rights_str_mv openAccess
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identifier_str_mv 10.36097/rsan.v1iEspecial_2.2826
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publishDate 2025
publisher.none.fl_str_mv Universidad San Gregorio de Portoviejo
reponame_str Revista Universidad San Gregorio de Portoviejo
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repository.name.fl_str_mv Revista Universidad San Gregorio de Portoviejo - Universidad San Gregorio de Portoviejo
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spelling Identification of parameters in ordinary differential equation systems using artificial neural networksIdentificación de parámetros en sistemas ecuaciones diferenciales ordinarias mediante el uso de redes neuronales artificialesDuque Aldaz, Francisco Javier Rodríguez-Flores, Fernando Raúl Carmona Tapia, José Duque Aldaz, Francisco JavierNumerical methodsparameter tuningartificial neural networksBackpropagation algorithmLevenberg-Marquardt methodMétodos numéricosajuste de parámetroredes neuronales artificialesalgoritmo Backpropagationmétodo Levenberg-MarquardtThe identification of parameters in systems of differential equations has been a complex scientific challenge, with limited traditional methods for modeling nonlinear physical phenomena and inconsistent experimental data. The objective was to compare an artificial neural network trained with Backpropagation and optimized by Levenberg-Marquardt against classical numerical methods to identify parameters in ordinary differential equations. A multilayer neural network was designed with one input, a hidden layer of 10 neurons and two outputs. The model was trained with experimental data divided into training, validation and test sets, using the Levenberg-Marquardt algorithm to fit its parameters. Accuracy was evaluated by comparing with the Runge-Kutta-based numerical method ODE45. The neural network demonstrated superior performance, achieving an accurate and less computationally complex approximation. While the ODE45 method presented good overall fits, it showed limitations at specific intervals due to spikes and discontinuities in the simulated functions. The neural network exhibited robustness in handling nonlinear dynamics, predicting with high accuracy the behavior of the system without requiring an explicit mathematical model. Its ability to recognize complex patterns with tolerable error margins consolidated it as an effective tool for dynamic systems. In conclusion, artificial neural networks were confirmed as a robust methodological alternative, allowing the modeling of dynamic nonlinear systems with simplicity, flexibility and scalability potential.La identificación de parámetros en sistemas de ecuaciones diferenciales constituyó un desafío científico complejo, con métodos tradicionales limitados para modelar fenómenos físicos no lineales y datos experimentales inconsistentes. El objetivo fue comparar una red neuronal artificial entrenada con Backpropagation y optimizada mediante Levenberg-Marquardt contra métodos numéricos clásicos para identificar parámetros en ecuaciones diferenciales ordinarias. Se diseñó una red neuronal multicapa con una entrada, una capa oculta de 10 neuronas y dos salidas. El modelo se entrenó con datos experimentales divididos en conjuntos de entrenamiento, validación y prueba, utilizando el algoritmo Levenberg-Marquardt para ajustar sus parámetros. La precisión se evaluó comparando con el método numérico ODE45, basado en Runge-Kutta. La red neuronal demostró un rendimiento superior, logrando una aproximación precisa y menos compleja computacionalmente. Mientras el método ODE45 presentó buenos ajustes generales, mostró limitaciones en intervalos específicos debido a picos y discontinuidades en las funciones simuladas. La red neuronal exhibió robustez para manejar dinámicas no lineales, prediciendo con alta precisión el comportamiento del sistema sin requerir un modelo matemático explícito. Su capacidad para reconocer patrones complejos con márgenes de error tolerables la consolidó como una herramienta eficaz para sistemas dinámicos. En conclusión, las redes neuronales artificiales se confirmaron como una alternativa metodológica robusta, permitiendo modelar sistemas no lineales dinámicos con simplicidad, flexibilidad y potencial de escalabilidad.Universidad San Gregorio de Portoviejo2025-02-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículo evaluado por paresapplication/pdfhttps://revista.sangregorio.edu.ec/index.php/REVISTASANGREGORIO/article/view/282610.36097/rsan.v1iEspecial_2.2826Revista San Gregorio; Vol. 1 No. Especial_2 (2025): Revista San Gregorio; 15-23Revista San Gregorio; Vol. 1 Núm. Especial_2 (2025): Revista San Gregorio; 15-232528-79071390-724710.36097/rsan.v1iEspecial_2reponame:Revista Universidad San Gregorio de Portoviejoinstname:Universidad San Gregorio de Portoviejoinstacron:USGPspahttps://revista.sangregorio.edu.ec/index.php/REVISTASANGREGORIO/article/view/2826/1730Derechos de autor 2025 Francisco Javier Duque Aldaz, Fernando Raúl Rodríguez-Flores, José Carmona Tapiahttp://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccess2025-12-15T14:42:28Zoai:ojs.pkp.sfu.ca:article/2826Portal de revistashttps://revista.sangregorio.edu.ec/Universidad privadahttps://sangregorio.edu.ec/..Ecuador.2528-79071390-7247opendoar:02025-12-15T14:42:28Revista Universidad San Gregorio de Portoviejo - Universidad San Gregorio de Portoviejofalse
spellingShingle Identification of parameters in ordinary differential equation systems using artificial neural networks
Duque Aldaz, Francisco Javier
Numerical methods
parameter tuning
artificial neural networks
Backpropagation algorithm
Levenberg-Marquardt method
Métodos numéricos
ajuste de parámetro
redes neuronales artificiales
algoritmo Backpropagation
método Levenberg-Marquardt
status_str publishedVersion
title Identification of parameters in ordinary differential equation systems using artificial neural networks
title_full Identification of parameters in ordinary differential equation systems using artificial neural networks
title_fullStr Identification of parameters in ordinary differential equation systems using artificial neural networks
title_full_unstemmed Identification of parameters in ordinary differential equation systems using artificial neural networks
title_short Identification of parameters in ordinary differential equation systems using artificial neural networks
title_sort Identification of parameters in ordinary differential equation systems using artificial neural networks
topic Numerical methods
parameter tuning
artificial neural networks
Backpropagation algorithm
Levenberg-Marquardt method
Métodos numéricos
ajuste de parámetro
redes neuronales artificiales
algoritmo Backpropagation
método Levenberg-Marquardt
url https://revista.sangregorio.edu.ec/index.php/REVISTASANGREGORIO/article/view/2826