Title :
Function approximation using backpropagation and general regression neural networks
Author :
Marquez, Leorey ; Hill, Tim
Author_Institution :
Hawaii Univ., Honolulu, HI, USA
Abstract :
The approximation capabilities of backpropagation (BP) neural networks and D. Specht´s (1991) general regression neural network (GRNN) are compared using data generated from 14 functions under three levels of random noise. The results show that the BP approach provides significantly more accurate estimates than the GRNN approach, especially when the level of random noise in the data is low
Keywords :
backpropagation; function approximation; mathematics computing; neural nets; random noise; backpropagation; function approximation; general regression neural networks; random noise; Backpropagation; Biological neural networks; Brain modeling; Function approximation; Humans; Least squares approximation; Neural networks; Noise generators; Noise level; Parameter estimation;
Conference_Titel :
System Sciences, 1993, Proceeding of the Twenty-Sixth Hawaii International Conference on
Conference_Location :
Wailea, HI
Print_ISBN :
0-8186-3230-5
DOI :
10.1109/HICSS.1993.284240
