Inferring the fractional nature of Wu Baleanu trajectories
[EN] We infer the parameters of fractional discrete Wu Baleanu time series by using machine learning architectures based on recurrent neural networks. Our results shed light on howclearly one can determine that a given trajectory comes from a specific fractional discrete dynamical system by estimati...
| Autores: | , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2023 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositório: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglês |
| OAI Identifier: | oai:riunet.upv.es:10251/204627 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/204627 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Fractional dynamical systems Chaotic systems Machine learning Recurrent neural networks MATEMATICA APLICADA 04.- Garantizar una educación de calidad inclusiva y equitativa, y promover las oportunidades de aprendizaje permanente para todos |
| Resumo: | [EN] We infer the parameters of fractional discrete Wu Baleanu time series by using machine learning architectures based on recurrent neural networks. Our results shed light on howclearly one can determine that a given trajectory comes from a specific fractional discrete dynamical system by estimating the fractional exponent and the growth parameter mu. With this example, we also show how machine learning methods can be incorporated into the study of fractional dynamical systems. |
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