New learning factor and testing methods for conjugate gradient training algorithm

Author

Kim, Tae ; Manry, Michael T. ; Maldonado, Javier

Author_Institution

Dept. of Electr. Eng., Texas Univ., Arlington, TX, USA

Volume

3

fYear

2003

fDate

20-24 July 2003

Firstpage

2011

Abstract

The conjugate gradient method has advantages over backpropagation in the training of artificial neural networks. Unlike previous investigators who have obtained learning factors using computationally expensive iterative line searches, we obtain the optimal learning factor in one step. We validate the learning factor with several tests, and analyze the input bias problem. Examples confirm the usefulness of improved conjugate gradient.

Keywords

backpropagation; conjugate gradient methods; learning (artificial intelligence); multilayer perceptrons; artificial neural networks training; backpropagation; conjugate gradient method; conjugate gradient training algorithm; input bias problem; iterative line searches; learning factors; multilayer perceptron; optimal learning factor; testing methods; Artificial neural networks; Character generation; Gradient methods; Image processing; Joining processes; Multilayer perceptrons; Optimization methods; Power system modeling; Predictive models; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223716

Filename

1223716