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2021 | OriginalPaper | Buchkapitel

Uncertainty Quantification in Data Fitting Neural and Hilbert Networks

verfasst von : Leila Khalij, Eduardo Souza de Cursi

Erschienen in: Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling

Verlag: Springer International Publishing

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Abstract

We analyze the uncertainties in Neural and Hilbert networks. The first source of uncertainty is the variability of partition of the data in subsets for training, testing and validating. The analysis is made on the variability of the performance and of the weights of the networks. The effects of additive and multiplicative noises in the data are studied. The results of Neural and Hilbert Networks are compared. It appears that Hilbert Networks are more robust but imply a higher computational cost. The distributions of the outputs are studied and it appears that their means and modes may be used to improve the estimates furnished by the nets. The extension of Hilbert Networks to Element Based Networks is considered.

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Titel: Uncertainty Quantification in Data Fitting Neural and Hilbert Networks
verfasst von: Leila Khalij
Eduardo Souza de Cursi
Verlag: Springer International Publishing
Buch: Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling
Print ISBN: 978-3-030-53668-8

Electronic ISBN: 978-3-030-53669-5

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-53669-5_17

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