Constructive function approximation: theory and practice

In this paper we study the theoretical limits of finite constructive convex approximations of a given function in a Hilbert space using elements taken from a reduced subset. We also investigate the trade-off between the global error and the partial error during the iterations of the solution. These results are then specialized to constructive function approximation using sigmoidal neural networks. The emphasis then shifts to the implementation issues associated with the problem of achieving given approximation errors when using a finite number of nodes and a finite data set for training

Neural networks in fault detection: a case study

We study the applications of neural nets in the area of fault detection in real vibrational data. The study is one of the first to include a large set of real vibrational data and to illustrate the potential as well as the limitations of neural networks for fault detection