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A recurrent self-organizing map for temporal sequence processing

  • Markus Varstal
  • José del R. Millán
  • Jukka Heikkonen
Part III: Learning: Theory and Algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)

Abstract

This paper presents a recurrent self-organizing map (RSOM) for temporal sequence processing. The RSOM uses the history of a pattern (i.e., the previous elements in the sequence) to compute the best matching unit and to adapt the weights of the map. The RSOM is similar to Kohonen's original SOM except that each unit has an associated recursive differential equation. The experimental results show that the RSOM is able to learn and distinguish temporal sequences, and that it can improve EEG-based epileptic activity detection.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Markus Varstal
    • 1
  • José del R. Millán
    • 2
  • Jukka Heikkonen
    • 1
  1. 1.Helsinki University of TechnologyLaboratory of Computational EngineeringEspooFinland
  2. 2.Joint Research Centre of the European CommissionIspra (VA)Italy

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