Neural Networks Organizations to Learn Complex Robotic Functions
Gilles Hermann, Patrice Wira, Jean-Philippe Urban · 2003
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. 1