Predictive deep learning models for analyzing discrete fractional dynamics from noisy and incomplete data

Garibo-i-Orts, OscarLizama, CarlosAkgul, AliConejero, J. Alberto2024-12-242024-12-2420240577-9073https://doi.org/10.1016/j.cjph.2024.04.010https://hdl.handle.net/20.500.12604/6435We 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.eninfo:eu-repo/semantics/openAccessDynamical systemsDiscrete fractional calculusWu-Baleanu modelLogistic mapConvolutional neural networksLSTM networksPredictive deep learning models for analyzing discrete fractional dynamics from noisy and incomplete dataArticle8912761285N/AWOS:001233807100001Q12-s2.0-8519088688910.1016/j.cjph.2024.04.010