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Fast multilevel evaluation of smooth radial basis function expansions.

Livne, Oren E., Wright, Grady B. (2006)

ETNA. Electronic Transactions on Numerical Analysis [electronic only]

Fehlerabschätzungen für Koeffizienten von Exponentialsummen und Polynomen.

R. Schaback (1982)

Numerische Mathematik

Function approximation of Seidel aberrations by a neural network

Rossella Cancelliere, Mario Gai (2004)

Bollettino dell'Unione Matematica Italiana

This paper deals with the possibility of using a feedforward neural network to test the discrepancies between a real astronomical image and a predefined template. This task can be accomplished thanks to the capability of neural networks to solve a nonlinear approximation problem, i.e. to construct an hypersurface that approximates a given set of scattered data couples. Images are encoded associating each of them with some conveniently chosen statistical moments, evaluated along the $\{x, y\}$ axes; in this...

Functions of the form $\sum_{i = 1}^{N} f_{i} (x) g_{i} (t)$ in $L_{2}$

František Neuman (1982)

Archivum Mathematicum

Displaying 1 – 4 of 4

Fast multilevel evaluation of smooth radial basis function expansions.

Fehlerabschätzungen für Koeffizienten von Exponentialsummen und Polynomen.

Function approximation of Seidel aberrations by a neural network

Functions of the form ∑ i = 1 N f i ( x ) g i ( t ) in L 2

Currently displaying 1 – 4 of 4

Functions of the form $\sum_{i = 1}^{N} f_{i} (x) g_{i} (t)$ in $L_{2}$