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  4. A Dynamical Implementation of Self-organizing Maps
 
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1994
Conference Paper
Title

A Dynamical Implementation of Self-organizing Maps

Abstract
The standard learning algorithm for self-organizing maps (SOM) involves the two steps of a search for the best matching neuron and of an update of its weight vectors in the neighborhood of this neuron. In the dynamical implementation discussed here, a competitive dynamics of laterally coupled neurons with diffusive interaction is used to find the best-matching neuron. The resulting neuronal excitation bubbles are used to drive a Hebbian learning algorithm that is similar to the one Kohonen uses. Convergence of the SOM is achieved here by relating time (or number of training steps) to the strength of the diffusive coupling. A standard application of the SOM is used to demonstrate the feasibility of the approach.
Author(s)
Banzhaf, W.
Schmutz, M.
Mainwork
ICASSE '94. Proceedings of the First International Conference on Applied Synergetic and Synergetic Engineering  
Conference
International Conference on Applied Synergetic and Synergetic Engineering (ICASSE) 1994  
Language
English
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA  
Keyword(s)
  • neuronal network

  • neuronales Netzwerk

  • self-organizing Maps (SOM)

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