Fitting 3D Data Points By Extending The Neural Networks Paradigm
Author(s)
A. Iglesias, A. Galvez
Abstract
Fitting 3D data points by extending
the neural networks paradigm
A. Iglesias, A. Galvez
Dept. of Applied Mathematics and Computational Sciences,
University of Cantabria, Spain
Abstract
This paper describes a new method to fit 3D data points from engineer-
ing environments (for example, from a numerical-controlled machine or by
digitizing a real model). Since these points are subjected to measurement
errors, approximation techniques are required. Among them, several tech-
niques based on neural networks have been extensively applied in the last
recent years. However, for data points from a B-spline surface the approx-
imation problem could not be well described in terms of neural networks.
To overcome this limitation, this paper introduces a new extension of the
neural networks paradigm, the functional networks, in which the weights
are replaced by neural functions with a multivariate character. In addition,
these neural functions can be different for different neurons. The perfor-
Keywords
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