Approximation of a function and its derivative with a neural network
Cardaliaguet, Pierre; Euvrard, Guillaume (1992), Approximation of a function and its derivative with a neural network, Neural Networks, 5, 2, p. 207-220. http://dx.doi.org/10.1016/S0893-6080(05)80020-6
TypeArticle accepté pour publication ou publié
Journal nameNeural Networks
MetadataShow full item record
Abstract (EN)This paper deals with the approximation of both a function and its derivative by feedforward neural networks. We propose an explicit formula of approximation which is noise resistant and can be easily modified with the patterns. We apply these results to approach a function defined implicitly, which is useful in control theory.
Subjects / KeywordsFeedforward neural networks; Functions approximation; Interpolation; Bell-shaped functions; Squashing functions; Robustness with respect to noise; Implicit function; Control
Showing items related by title and author.
Comparison of the Predictability of a Neural Network with Retropropagation with Those using Linear Regression, Logistic and A.I.D. Methods for Direct Marketing Scoring Desmet, Pierre (1998) Chapitre d'ouvrage
Cardaliaguet, Pierre; Forcadel, Nicolas (2021) Document de travail / Working paper
Regularity of the value function and quantitative propagation of chaos for mean field control problems Cardaliaguet, Pierre; Souganidis, Panagiotis E. (2022) Document de travail / Working paper
Deep learning of Value at Risk through generative neural network models : the case of the Variational Auto Encoder Brugière, Pierre; Turinici, Gabriel (2022) Document de travail / Working paper
Araujo, Alexandre (2021-06-01) Thèse