Predictive deep learning models for analyzing discrete fractional dynamics from noisy and incomplete data
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Dynamical systems, Discrete fractional calculus, Wu-Baleanu model, Logistic map, Convolutional neural networks, LSTM networks
Kaynak
Chinese Journal of Physics
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
89
