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 how clearly one can determine that a given trajectory comes from a specific fractional discrete dynamical system by estimating the fractional exponent and the growth parameter μ. With this example, we also show how machine learning methods can be incorporated into the study of fractional dynamical systems.
We have published this work in Nonlinear Dynamics as a Gold Open Access publication with the support of CRUE-Universitat Politècnica de València. The reference is
J. Alberto Conejero, Ò. Garibo-i-Orts, C. Lizama. Inferring the fractional nature of Wu Baleanu trajectories. Nonlinear Dyn. doi:10.1007/s11071-023-08463-1
