Abstract
Self-organising neural networks try to preserve the topology of an input space by means of their competitive learning. This capacity has been used, among others, for the representation of objects and their motion. In this work we use a kind of self-organising network, the Growing Neural Gas, to represent different objects shape. As a result of an adaptive process the objects are represented by a topology representing graph that constitutes an induced Delaunay triangulation of their shapes. This feature can be used to learn and represent topologic maps that mobile devices use to navigate in different environments.
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García-Rodríguez, J., Flórez-Revuelta, F., Manuel García-Chamizo, J. (2007). Learning Topologic Maps with Growing Neural Gas. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74827-4_59
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DOI: https://doi.org/10.1007/978-3-540-74827-4_59
Publisher Name: Springer, Berlin, Heidelberg
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