Title :
Neural networks for function approximation
Author :
Mhaskar, H.N. ; Khachikyan, L.
Author_Institution :
Dept. of Math., California State Univ., Los Angeles, CA, USA
fDate :
31 Aug-2 Sep 1995
Abstract :
We describe certain results of Mhaskar concerning the approximation capabilities of neural networks with one hidden layer. In particular, these results demonstrate the construction of neural networks evaluating a squashing function or a radial basis function for optimal approximation of the Sobolev spaces. We also report on the application of some of these ideas in the construction of general-purpose networks for the prediction of time series, when the number of independent variables is known in advance, such as the Mackey-Glass series or the flour data
Keywords :
function approximation; neural nets; optimisation; Mackey-Glass series; Sobolev spaces; flour data; function approximation; neural networks; optimal approximation; radial basis function; squashing function; time series prediction; Algorithm design and analysis; Biological neural networks; Function approximation; Glass; Mathematics; Neural networks; Neurons; Sampling methods; Size measurement; Terminology;
Conference_Titel :
Neural Networks for Signal Processing [1995] V. Proceedings of the 1995 IEEE Workshop
Conference_Location :
Cambridge, MA
Print_ISBN :
0-7803-2739-X
DOI :
10.1109/NNSP.1995.514875
