Function approximation using backpropagation and general regression neural networks

Author

Marquez, Leorey ; Hill, Tim

Author_Institution

Hawaii Univ., Honolulu, HI, USA

fYear

1993

fDate

5-8 Jan 1993

Firstpage

607

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;

fLanguage

English

Publisher

ieee

Conference_Titel

System Sciences, 1993, Proceeding of the Twenty-Sixth Hawaii International Conference on

Conference_Location

Wailea, HI

Print_ISBN

0-8186-3230-5

Type

conf

DOI

10.1109/HICSS.1993.284240

Filename

284240

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1802114