Predictive deep learning models for analyzing discrete fractional dynamics from noisy and incomplete data
[EN] We study the accuracy of machine learning methods for inferring the parameters of noisy fractional Wu¿Baleanu trajectories with some missing initial terms. Our model is based on a combination of convolutional and recurrent neural networks (LSTM), which permits the extraction of characteristics...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/214758 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/214758 |
| Access Level: | acceso abierto |
| Palabra clave: | Dynamical systems Discrete fractional calculus Wu Baleanu model Logistic map Convolutional neural networks LSTM networks MATEMATICA APLICADA 04.- Garantizar una educación de calidad inclusiva y equitativa, y promover las oportunidades de aprendizaje permanente para todos |
| Sumario: | [EN] We study the accuracy of machine learning methods for inferring the parameters of noisy fractional Wu¿Baleanu trajectories with some missing initial terms. Our model is based on a combination of convolutional and recurrent neural networks (LSTM), which permits the extraction of characteristics from trajectories while preserving time dependency. We show that these approach exhibit good accuracy results despite the poor quality of the data. |
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