Successive approximation training algorithm for feedforward neural networks

Citation
Yc. Liang et al., Successive approximation training algorithm for feedforward neural networks, NEUROCOMPUT, 42, 2002, pp. 311-322
Citations number
19
Language
INGLESE
art.tipo
Article
Categorie Soggetti
AI Robotics and Automatic Control
Journal title
NEUROCOMPUTING
ISSN journal
0925-2312 → ACNP
Volume
42
Year of publication
2002
Pages
311 - 322
Database
ISI
SICI code
0925-2312(200201)42:<311:SATAFF>2.0.ZU;2-9
Abstract
A novel algorithm based on successive approximation training for feedforwar d neural networks is presented in this paper. The convergence of the algori thm is analysed theoretically and the training error is estimated. Theoreti cal analysis shows that the novel training algorithm is able to overcome th e stalemate problem in the later training stage of the traditional algorith ms. Numerical experiments show that the proposed algorithm increases the ra te of convergence and improves the generalization performance by avoiding l ocal minima. (C) 2002 Elsevier Science B.V. All rights reserved.