Next: Introduction
Parallel Back-Propagation for Sales Prediction on Transputer Systems
Frank M. Thiesing, Ulrich Middelberg, Oliver Vornberger
University of Osnabrück, Dept. of Math./Comp. Science
D-49069 Osnabrück, Germany
Frank.Thiesing@GAD.de
Abstract:
In this paper artificial neural networks are adapted to a short term forecast for the sale of articles
in supermarkets.
The data is modelled to fit into feedforward multilayer perceptron networks that are trained by
the back-propagation algorithm.
For enhancement this has been parallelized in different manners.
One batch and two on-line training variants are implemented
on parallel Transputer-based PARSYTEC systems: a GCel with T805
and a GC/PP with PowerPC processors and Transputer communication links.
The parallelizations run with both the runtime environments PARIX and PVM.
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