In this paper, a novel unsupervised neural network combining elements from Adaptive Resonance Theory and topology learning neural networks, in particular the Self-Organising Incremental Neural Network, is introduced. It enables stable on-line clustering of stationary and non-stationary input data. In addition, two representations reflecting different levels of detail are learnt simultaneously. Furthermore, the network is designed in such a way that its sensitivity to noise is diminished, which renders it suitable for the application to real-world problems. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Tscherepanow, M. (2010). TopoART: A topology learning hierarchical ART network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6354 LNCS, pp. 157–167). https://doi.org/10.1007/978-3-642-15825-4_21
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