TopoART: A Topology Learning Hierarchical ART Network

  • Marko Tscherepanow
Conference paper

DOI: 10.1007/978-3-642-15825-4_21

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6354)
Cite this paper as:
Tscherepanow M. (2010) TopoART: A Topology Learning Hierarchical ART Network. In: Diamantaras K., Duch W., Iliadis L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marko Tscherepanow
    • 1
  1. 1.Applied InformaticsBielefeld UniversityBielefeldGermany

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