DocumentCode
1395808
Title
A fast training algorithm for neural networks
Author
Bilski, Jaroslaw ; Rutkowski, Leszek
Author_Institution
Dept. of Comput. Eng., Tech. Univ. of Czestochowa, Poland
Volume
45
Issue
6
fYear
1998
fDate
6/1/1998 12:00:00 AM
Firstpage
749
Lastpage
753
Abstract
The recursive least squares method (RLS) is derived for the learning of multilayer feedforward neural networks. Simulation results on the XOR, 4-2-4 encoder, and function approximation problems indicate a fast learning process in comparison to the classical and momentum backpropagation (BP) algorithms
Keywords
encoding; feedforward neural nets; function approximation; learning (artificial intelligence); least squares approximations; 4-2-4 encoder problem; RLS method; XOR problem; fast learning process; fast training algorithm; function approximation problem; multilayer feedforward neural networks; neural networks; recursive least squares method; Backpropagation algorithms; Feedforward neural networks; Function approximation; Least squares methods; Multi-layer neural network; Neural networks; Neurons; Resonance light scattering; Terminology; Vectors;
fLanguage
English
Journal_Title
Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on
Publisher
ieee
ISSN
1057-7130
Type
jour
DOI
10.1109/82.686696
Filename
686696
Link To Document