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- W1482149632 abstract "This paper considers the general problem of function es- timation 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 subnet- works 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." @default.
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- W1482149632 date "2003-01-01" @default.
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- W1482149632 title "Neural Networks Organizations to Learn Complex Robotic Functions" @default.
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