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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2025
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| Διαθέσιμο Online: | https://revista.sangregorio.edu.ec/index.php/REVISTASANGREGORIO/article/view/2826 |
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| _version_ | 1858437124874829824 |
|---|---|
| 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 |
| format | article |
| id | REVUSGP_b8e4b89167dbfdef62118e671d686821 |
| identifier_str_mv | 10.36097/rsan.v1iEspecial_2.2826 |
| instacron_str | USGP |
| institution | USGP |
| instname_str | Universidad San Gregorio de Portoviejo |
| language | spa |
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| network_name_str | Revista Universidad San Gregorio de Portoviejo |
| oai_identifier_str | oai:ojs.pkp.sfu.ca:article/2826 |
| publishDate | 2025 |
| publisher.none.fl_str_mv | Universidad San Gregorio de Portoviejo |
| reponame_str | Revista Universidad San Gregorio de Portoviejo |
| repository.mail.fl_str_mv | . |
| repository.name.fl_str_mv | Revista Universidad San Gregorio de Portoviejo - Universidad San Gregorio de Portoviejo |
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| rights_invalid_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0 |
| 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 |