Rechercher

[BSM01] A general formulation of non-linear least square regression using multi-layered perceptrons

Rapport Scientifique : Date de dépot: 2001/01/01, (Tech. Rep.: CEDRIC-01-243)
motcle:
Résumé: Non linear regression and non linear approximation are widely used for data analysis. In many applications, the aim is to build a model linking observations and parameters of a physical system. Two cases of increasing complexity have been studied: the case of deterministic inputs and noisy output data and the case of noisy input and output data. We present in this paper a general formulation of non linear regression using multilayered Perceptrons. Regression algorithms are derived in the three cases. In particular, a generalized learning rule is proposed to deal with noisy input and output data. The algorithm enables not only to build an accurate model but also to re*ne the learning data set. The algorithms are tested on two real-world problem in Geophysics. The good results suggests that multilayered Perceptrons can emmerged as an e°cient nonlinear regression model for a wide range of applications.

Commentaires: Rapport Scientifique

Collaboration: UPMC

BibTeX

@techreport {
BSM01,
title="{A general formulation of non-linear least square regression using multi-layered perceptrons }",
author="F. Badran and Y. Stphan and N Metoui and S. Thiria",
number="{CEDRIC-01-243}",
institution="{CEDRIC laboratory, CNAM-Paris, France}",
date={01-01-2001},
note="{Rapport Scientifique}",
}