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.
CITATION STYLE
Barbiero, P., Cirrincione, G., Cirrincione, M., Piccolo, E., & Vaccarino, F. (2020). Neural Epistemology in Dynamical System Learning. In Smart Innovation, Systems and Technologies (Vol. 151, pp. 213–221). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-13-8950-4_20
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