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Geometrical Initialization, Parametrization and Control of Multilayer Perceptrons: Application to Function Approximation

By Fabrice Rossi and Cedric GEGOUT

Abstract

This paper proposes a new method to reduce training time for neural nets used as function approximators. This method relies on a geometrical control of Multilayer Perceptrons (MLP). A geometrical initialization gives first better starting points for the learning process. A geometrical parametrization achieves then a more stable convergence. During the learning process, a dynamic geometrical control helps to avoid local minima. Finally, simulation results are presented, showing drastic reduction in training time and increase in convergence rate

Publisher: IEEE
Year: 1994
OAI identifier: oai:CiteSeerX.psu:10.1.1.18.4969
Provided by: CiteSeerX
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