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Design and Implementation of Parallel Counterpropagation Networks Using MPI

Design and Implementation of Parallel Counterpropagation Networks Using MPI The objective of this research is to construct parallel models that simulate the behavior of artificial neural networks. The type of network that is simulated in this project is the counterpropagation network and the parallel platform used to simulate that network is the message passing interface (MPI). In the next sections the counterpropagation algorithm is presented in its serial as well as its parallel version. For the latter case, simulation results are given for the session parallelization as well as the training set parallelization approach. Regarding possible parallelization of the network structure, there are two different approaches that are presented; one that is based to the concept of the intercommunicator and one that uses remote access operations for the update of the weight tables and the estimation of the mean error for each training stage. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Informatica IOS Press

Design and Implementation of Parallel Counterpropagation Networks Using MPI

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Publisher
IOS Press
Copyright
Copyright © 2007 by IOS Press, Inc
ISSN
0868-4952
eISSN
1822-8844
Publisher site
See Article on Publisher Site

Abstract

The objective of this research is to construct parallel models that simulate the behavior of artificial neural networks. The type of network that is simulated in this project is the counterpropagation network and the parallel platform used to simulate that network is the message passing interface (MPI). In the next sections the counterpropagation algorithm is presented in its serial as well as its parallel version. For the latter case, simulation results are given for the session parallelization as well as the training set parallelization approach. Regarding possible parallelization of the network structure, there are two different approaches that are presented; one that is based to the concept of the intercommunicator and one that uses remote access operations for the update of the weight tables and the estimation of the mean error for each training stage.

Journal

InformaticaIOS Press

Published: Jan 1, 2007

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