Neural Epistemology in Dynamical System Learning

Pietro Barbiero (1) , Giansalvo Cirrincione (2) , Maurizio Cirrincione (3) , Elio Piccolo , Francesco Vaccarino (4, 5)

1 CAM - University of Cambridge [UK]
2 LTI - Laboratoire des technologies innovantes - UR UPJV 3899
3 USP - University of the South Pacific
4 Polito - Politecnico di Torino = Polytechnic of Turin
5 Institute for Scientific Interchange (ISI) Foundation

In the last few years, neural networks are effectively applied in different fields. However, the application of empirical-like algorithms as feed-forward neural networks is not always justified from an epistemological point of view [1]. In this work, the assumptions for the appropriate application of machine learning empirical-like algorithms to dynamical system learning are investigated from a theoretical perspective. A very simple example shows how the suggested analyses are crucial in corroborating or discrediting machine learning outcomes.

Domaines: Sciences de l'ingénieur [physics]

Pietro Barbiero, Giansalvo Cirrincione, Maurizio Cirrincione, Elio Piccolo, Francesco Vaccarino. Neural Epistemology in Dynamical System Learning. Esposito, A and FaundezZanuy, M and Morabito, FC and Pasero, E. NEURAL APPROACHES TO DYNAMICS OF SIGNAL EXCHANGES, 151, pp.213-221, 2020, Smart Innovation, Systems and Technologies, 978-981-13-8950-4; 978-981-13-8949-8. ⟨10.1007/978-981-13-8950-4\_20⟩. ⟨hal-03631432⟩