On the composite squared-error algorithm for neural networks

Author

Netto, Sergio L. ; De Campos, Marcello L R

Author_Institution

COPPE, Univ. Fed. do Rio de Janeiro, Brazil

Volume

3

fYear

2000

fDate

2000

Firstpage

149

Abstract

The composite squared-error (CSE) algorithm results from the combination of the backpropagation algorithm with the pseudo-linear algorithm of Scalero and Tepedelenlioglu (1992). It is observed that the CSE algorithm is able to avoid suboptimal solutions and associated saddle points, thus achieving lower values of the error function than the pseudo-linear algorithm, in fewer iterations than the backpropagation algorithm. In this paper, we investigate the implementation of the CSE algorithm with the concepts of momentum gain, time-varying learning rate, and time-varying combining factor. It is verified that these features can improve the overall properties of the CSE convergence process

Keywords

convergence; learning (artificial intelligence); neural nets; backpropagation algorithm; composite squared-error algorithm; convergence process; error function; momentum gain; neural networks; pseudo-linear algorithm; time-varying combining factor; time-varying learning rate; Backpropagation algorithms; Convergence; Error correction; Feedforward neural networks; Neural networks; Neurons; Signal processing; Signal processing algorithms; Steady-state;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2000. Proceedings. ISCAS 2000 Geneva. The 2000 IEEE International Symposium on

Conference_Location

Geneva

Print_ISBN

0-7803-5482-6

Type

conf

DOI

10.1109/ISCAS.2000.856018

Filename

856018