Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
566370 | Advances in Engineering Software | 2009 | 6 Pages |
Abstract
This paper presents an incremental learning algorithm for feed-forward neural networks used as approximators of real world data. This algorithm allows neural networks of limited size to be obtained, providing better performances. The algorithm is compared to two of the main incremental algorithms (Dunkin and cascade correlation) in the respective contexts of synthetic data and of real data consisting of radiation doses in homogeneous environments.
Related Topics
Physical Sciences and Engineering
Computer Science
Software
Authors
Jacques M. Bahi, Sylvain Contassot-Vivier, Marc Sauget,