Author
Cardaliaguet, Pierre
Euvrard, Guillaume
Type
Article accepté pour publication ou publié
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.
