DocumentCode
324563
Title
Asymptotic approximation power for neural networks
Author
Ferreira, Paulo J S G ; Pinho, Armando J.
Author_Institution
Univ. of Aveiro, Portugal
Volume
2
fYear
1998
fDate
4-9 May 1998
Firstpage
1261
Abstract
This paper studies the asymptotic approximation power of radial basis function neural networks in the sup norm. The methods used are constructive and based on discretization of approximate identities. The effect of the kernel on the approximation order is discussed
Keywords
computational complexity; convergence of numerical methods; feedforward neural nets; function approximation; approximation order; asymptotic approximation power; computational complexity; convergence; discretization; function approximation; radial basis function neural networks; Approximation error; Approximation methods; Convergence; Fourier series; Interpolation; Joining processes; Kernel; Neural networks; Nonlinear filters; Radial basis function networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location
Anchorage, AK
ISSN
1098-7576
Print_ISBN
0-7803-4859-1
Type
conf
DOI
10.1109/IJCNN.1998.685955
Filename
685955
Link To Document