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Chapitre d'ouvrage

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
Abstract : 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.
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Chapitre d'ouvrage
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https://hal-u-picardie.archives-ouvertes.fr/hal-03631432
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Soumis le : mardi 5 avril 2022 - 16:25:08
Dernière modification le : mercredi 6 avril 2022 - 03:00:16

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U-PICARDIE | LTI

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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⟩

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