NDLI: Neural network fields

Content Provider	IEEE Xplore Digital Library
Author	Pelletier, B.
Copyright Year	2003
Description	Author affiliation: Lab. de Mathematiques Appliquees, Univ. du Havre, Le Havre, France (Pelletier, B.)
Abstract	In this paper, a neural network field over a subset /spl Xi/ of a metric space and a corresponding stochastic learning algorithm are introduced. A neural network field is a neural network, the parameters of which are functions of other variables, being valued in /spl Xi/. Neural network fields are mostly dedicated to the problem of approximating a parametrized function or, more generally, to the problem of approximating a function field. Typical examples of this kind of problem may be found in the context of geophysical sciences, where the observed data depends on two or three angular variables characterizing the data acquisition process. Neural network fields also offers interesting perspectives within the field of parametric nonlinear modeling techniques.
Starting Page	167
Ending Page	176
File Size	424623
Page Count	10
File Format	PDF
ISBN	0780381777
ISSN	10893555
DOI	10.1109/NNSP.2003.1318015
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-17
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Extraterrestrial measurements Oceans Stochastic processes Data acquisition Input variables Color Geometry Sediments Remote sensing
Content Type	Text
Resource Type	Article

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