Parametric Validation of the Reservoir Computing–Based
Machine Learning Algorithm Applied to Lorenz System
Reconstructed Dynamics
Samuele Mazzi
Aix-Marseille Université, CNRS, PIIM, UMR 7345 Marseille, France
CEA, IRFM, F-13108 Saint-Paul-lez-Durance, France
David Zarzoso
Aix-Marseille Université, CNRS, PIIM, UMR 7345 Marseille, France
Aix-Marseille Université, CNRS, Centrale Marseille, M2P2 UMR 7340, Marseille, France
Abstract
A detailed parametric analysis is presented, where the recent method based on the reservoir computing paradigm, including its statistical robustness, is studied. It is observed that the prediction capabilities of the reservoir computing approach strongly depend on the random initialization of both the input and the reservoir layers. Special emphasis is put on finding the region in the hyperparameter space where the ensemble-averaged training and generalization errors together with their variance are minimized. The statistical analysis presented here is based on the projection on proper elements method.
Keywords: reservoir computing; Lorenz system; hyperparameters; error quantification; machine learning
Cite this publication as:
S. Mazzi and D. Zarzoso, “Parametric Validation of the Reservoir Computing–Based Machine Learning Algorithm Applied to Lorenz System Reconstructed Dynamics,” Complex Systems, 31(3), 2022 pp. 311–339.
https://doi.org/10.25088/ComplexSystems.31.3.311
