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NeuroCOLT
Technical Report NC-TR-95-017
Identification
of the Human Arm Kinetics using Dynamic Recurrent Neural Networks
Jean-Philippe
DRAYE,
Faculte Polytechnique de Mons,
Guy
CHERON,
University of Brussels,
Marc
BOURGEOIS,
University of Brussels,
Davor
PAVISIC,
Faculte} Polytechnique de Mons,
Gaetan
LIBERT,
Faculte Polytechnique de Mons
Abstract
Artificial neural networks offer an exciting alternative for
modeling and identi fying complex non-linear systems. This paper investigates
the identification of discrete-time non-linear systems using dynamic
recurrent neural networks. We use this kind of networks to efficiently
identify the complex temporal relati onship between the patterns of
muscle activation represented by the electromyogr aphy signal (EMG)
and their mechanical actions in three-dimensional space. The results
show that dynamic neural networks provide a successful platform for
biomechanical modeling and simulation including complex temporal relationships.
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