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[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. Stéphan and N Metoui and S. Thiria", | |
| number | = | "{CEDRIC-01-243}", | |
| institution | = | "{CEDRIC laboratory, CNAM-Paris, France}", | |
| date | = | {01-01-2001}, | |
| note | = | "{Rapport Scientifique}", | |
| } | |||
