A constructive neural network algorithm for function approximation

Author

Draelos, Tim ; Hush, Don

Author_Institution

Sandia Nat. Labs., Albuquerque, NM, USA

Volume

1

fYear

1996

fDate

3-6 Jun 1996

Firstpage

50

Abstract

A study of the approximation capabilities of single hidden layer neural networks leads to a strong motivation for investigating constructive learning techniques as a means of realizing established error bounds. Learning characteristics employed by constructive algorithms provide ideas for development of new algorithms applicable to the function approximation problem. A novel constructive algorithm, the iterative incremental function approximation (IIFA) algorithm is presented in detail. The algorithm operates in polynomial time and is demonstrated on one and two dimensional function approximation problems

Keywords

computational complexity; function approximation; iterative methods; neural nets; constructive neural network algorithm; established error bounds; iterative incremental function approximation; polynomial time algorithm; single hidden layer neural networks; Approximation algorithms; Approximation error; Computer errors; Costs; Function approximation; Iterative algorithms; Laboratories; Neural networks; Polynomials; Weather forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1996., IEEE International Conference on

Conference_Location

Washington, DC

Print_ISBN

0-7803-3210-5

Type

conf

DOI

10.1109/ICNN.1996.548865

Filename

548865