Adaptive inverse control
Self-organizing maps
Visually guided movements: learning with modular neural maps in robotics
Neural Networks - Special issue on neural control and robotics: biology and technology
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Computation and Self-Organizing Maps; An Introduction
Neural Computation and Self-Organizing Maps; An Introduction
A theoretical framework for multiple neural network systems
Neurocomputing
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The learning of complex relationships can be decomposed into several neural networks. The modular organization is determined by prior knowledge of the problem that permits to split the processing into tasks of small dimensionality. The sub-tasks can be implemented with neural networks, although the learning examples cannot be used anymore to supervise directly each of the networks. This article addresses the problem of learning in a modular context, developing in particular additive compositions. A simple rule allows defining efficient training, and combining, for example, several Supervised-SOM networks. This technique is important because it introduces interesting generalizations in many modular compositions, permitting data fusion or sequential combinations of neural networks.