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ESANN
2003

Neural networks organizations to learn complex robotic functions

8 years 4 months ago
Neural networks organizations to learn complex robotic functions
Abstract. This paper considers the general problem of function estimation with a modular approach of neural computing. We propose to use functionally independent subnetworks to learn complex functions. Thus, function approximation is decomposed and amounts to estimate different elementary sub-functions rather than the whole function with a single network. This modular decomposition is a way to introduce some a priori knowledge in neural estimation. Functionally independent subnetworks are obtained with a bidirectional learning scheme. Implemented with self-organizing maps, the modular approach has been applied to a robot control problem, a robot positioning task.
Gilles Hermann, Patrice Wira, Jean-Philippe Urban
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where ESANN
Authors Gilles Hermann, Patrice Wira, Jean-Philippe Urban
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