Topology representing networks
Neural Networks
A unifying objective function for topographic mappings
Neural Computation
Dynamic topology representing networks
Neural Networks
Self-Organizing Maps
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
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The paper proposes a method for visual based self-localisation of a mobile agent in indoor environment. The images acquired by the camera constitute an implicit topological representation of the environment. The environment is a priori unknown and so the implemented architecture is entirely unsupervised. To compare the performance of some self-organising neural networks, a similar neural network architecture of both Self-Organizing Map (SOM) and Growing Neural Gas (GNG) has been realized. Extensive simulations are provided to characterise the effectiveness of the GNG model in recognition speed, classification tasks and in particular topology preserving as compared to the SOM model. This behaviour depends on the following fact: a network (GNG) that adds nodes into map space can approximate the input space more accurately than a network with a predefined structure and size (SOM). The work shows that the GNG network is able to correctly reconstruct the environment topological map.