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Abstract

This paper presents a recurrent self-organizing map (RSOM) for temporal sequence processing. The RSOM uses the history of a pattern (ie 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 self-organizing map (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 electroencephalogram (EEG)-based epileptic activity detection.

Additional information

Authors: VARSTA M, Helsinki University of Technology, Laboratory of Computational Engineering, Espoo (FI);HEIKKONEN J, Helsinki University of Technology, Laboratory of Computational Engineering, Espoo (FI);MILLÁN J DEL R, JRC Ispra (IT)
Bibliographic Reference: Paper presented: 7th International Conference on Artificial Neural Networks, Lausanne (CH), 8-10 October 1997
Availability: Available from (1) as Paper EN 40934 ORA
Record Number: 199810198 / Last updated on: 1998-02-12
Category: PUBLICATION
Original language: en
Available languages: en