A Parallel Incremental Learning Algorithm for Neural Networks with Fault Tolerance
Jacques M. Bahi (LIFC, University of Franche-Comté, Belfort, France)
Sylvain Contassot-Vivier (LORIA, University Henri Poincaré, Nancy, France)
Marc Sauget (LIFC, University of Franche-Comté, Belfort, France)
Aurélien Vasseur (Femto-St, University of Franche-Comté, Montbéliard, France)
This paper presents a parallel and fault tolerant version of an incremental learning algorithm for feed-forward neural networks used as function approximators. It has been shown in previous works that our incremental algorithm builds networks of reduced size while providing high quality approximations for real data sets. However, for very large sets, the use of our learning process on a single machine may be quite long and even sometimes impossible, due to memory limitations. The parallel algorithm presented in this paper is usable in any parallel system, and in particular, with large dynamical systems such as clusters and grids in which faults may occur. Finally, the quality and performances (without and with faults) of that algorithm are experimentally evaluated.
Parallel and Distributed Computing, Cluster Computing, Grid Computing (middleware, algorithms, performance evaluation, ...), Computing in Healthcare and Biosciences
Toulouse | France | 2008 | June | 24  25  26  27