A robust and effective learning algorithm for feedforward neural networks based on the influence function
Allende
H
author
Salas
R
author
Moraga
C
author
2003
English
The learning process of the Feedforward Artificial Neural Networks relies on the data, though a robustness analysis of the parameter estimates of the model must be done due to the presence of outlying observations in the data. In this paper we seek the robust properties in the parameter estimates in the sense that the influence of aberrant observations or outliers in the estimate is bounded so the neural network is able to model the bulk of data. We also seek a trade off between robustness and efficiency under a Gaussian model. An adaptive learning procedure that seeks both aspects is developed. Finally we show some simulations results applied to the RESEX time series.
feedforward artificial neural networks
robust learning
effective parameter estimate
WOS:000184832300004
exported from refbase (show.php?record=35), last updated on Sat, 25 Jul 2009 00:43:13 -0400
text
files/35_Allende_etal2003.pdf
10.1007/b12122
Allende_etal2003
Lecture Notes in Computer Sciences
Lect. Notes Comput. Sc.
Pattern Recognition And Image Analysis
2003
Springer-Verlag Berlin
continuing
periodical
academic journal
2652
28
36
0302-9743