Topology representing networks
Neural Networks
Self-Organizing Maps
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Hand Gesture Recognition Following the Dynamics of a Topology-Preserving Network
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Surveillance and human-computer interaction applications of self-growing models
Applied Soft Computing
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Self-organizing neural networks try to preserve the topology of an input space by means of their competitive learning. This capacity is being used for the representation of objects and their motion. In addition, these applications usually have real-time constraints. In this work, diverse variants of a self-organizing network, the Growing Neural Gas, that allow an acceleration of the learning process are considered. However, this increase of speed causes that, in some cases, topology preservation is lost and, therefore, the quality of the representation. So, we have made a study to quantify topology preservation using different measures to establish the most suitable learning parameters, depending on the size of the network and on the available time for its adaptation.